mirror of https://github.com/vladmandic/human
update hand model
Vladimir Mandic
parent
1c0499d797
commit
a61b19ac0c
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@ -113,6 +113,7 @@ export default {
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scoreThreshold: 0.8, // threshold for deciding when to remove boxes based on score in non-maximum suppression
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enlargeFactor: 1.65, // empiric tuning as skeleton prediction prefers hand box with some whitespace
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maxHands: 1, // maximum number of hands detected in the input, should be set to the minimum number for performance
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landmarks: true, // detect hand landmarks or just hand boundary box
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detector: {
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modelPath: '../models/handdetect.json',
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},
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@ -27,6 +27,10 @@ const ui = {
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maxFrames: 10,
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modelsPreload: true,
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modelsWarmup: true,
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menuWidth: 0,
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menuHeight: 0,
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camera: {},
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fps: [],
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};
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// global variables
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@ -34,8 +38,6 @@ let menu;
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let menuFX;
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let worker;
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let timeStamp;
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let camera = {};
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const fps = [];
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// helper function: translates json to human readable string
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function str(...msg) {
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@ -62,17 +64,22 @@ const status = (msg) => {
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// draws processed results and starts processing of a next frame
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function drawResults(input, result, canvas) {
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// update fps data
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fps.push(1000 / (performance.now() - timeStamp));
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if (fps.length > ui.maxFrames) fps.shift();
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const elapsed = performance.now() - timeStamp;
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ui.fps.push(1000 / elapsed);
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if (ui.fps.length > ui.maxFrames) ui.fps.shift();
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// enable for continous performance monitoring
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// console.log(result.performance);
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// immediate loop before we even draw results, but limit frame rate to 30
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if (input.srcObject) {
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// eslint-disable-next-line no-use-before-define
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if (input.srcObject) requestAnimationFrame(() => runHumanDetect(input, canvas)); // immediate loop before we even draw results
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if (elapsed > 33) requestAnimationFrame(() => runHumanDetect(input, canvas));
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// eslint-disable-next-line no-use-before-define
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else setTimeout(() => runHumanDetect(input, canvas), 33 - elapsed);
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}
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// draw fps chart
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menu.updateChart('FPS', fps);
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menu.updateChart('FPS', ui.fps);
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// draw image from video
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const ctx = canvas.getContext('2d');
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ctx.fillStyle = ui.baseBackground;
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@ -94,9 +101,9 @@ function drawResults(input, result, canvas) {
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const gpu = engine.backendInstance ? `gpu: ${(engine.backendInstance.numBytesInGPU ? engine.backendInstance.numBytesInGPU : 0).toLocaleString()} bytes` : '';
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const memory = `system: ${engine.state.numBytes.toLocaleString()} bytes ${gpu} | tensors: ${engine.state.numTensors.toLocaleString()}`;
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const processing = result.canvas ? `processing: ${result.canvas.width} x ${result.canvas.height}` : '';
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const avg = Math.trunc(10 * fps.reduce((a, b) => a + b) / fps.length) / 10;
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const avg = Math.trunc(10 * ui.fps.reduce((a, b) => a + b) / ui.fps.length) / 10;
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document.getElementById('log').innerText = `
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video: ${camera.name} | facing: ${camera.facing} | resolution: ${camera.width} x ${camera.height} ${processing}
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video: ${ui.camera.name} | facing: ${ui.camera.facing} | resolution: ${ui.camera.width} x ${ui.camera.height} ${processing}
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backend: ${human.tf.getBackend()} | ${memory}
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performance: ${str(result.performance)} FPS:${avg}
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`;
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@ -147,7 +154,7 @@ async function setupCamera() {
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const track = stream.getVideoTracks()[0];
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const settings = track.getSettings();
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log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }, 'settings:', settings, 'track:', track);
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camera = { name: track.label, width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };
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ui.camera = { name: track.label, width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };
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return new Promise((resolve) => {
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video.onloadeddata = async () => {
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video.width = video.videoWidth;
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@ -156,6 +163,8 @@ async function setupCamera() {
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canvas.height = video.height;
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canvas.style.width = canvas.width > canvas.height ? '100vw' : '';
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canvas.style.height = canvas.width > canvas.height ? '' : '100vh';
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ui.menuWidth.input.setAttribute('value', video.width);
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ui.menuHeight.input.setAttribute('value', video.height);
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// silly font resizing for paint-on-canvas since viewport can be zoomed
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const size = 14 + (6 * canvas.width / window.innerWidth);
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ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);
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@ -351,8 +360,8 @@ function setupMenu() {
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menuFX.addHTML('<hr style="min-width: 200px; border-style: inset; border-color: dimgray">');
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menuFX.addLabel('Image Processing');
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menuFX.addBool('Enabled', human.config.filter, 'enabled');
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menuFX.addRange('Image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));
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menuFX.addRange('Image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));
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ui.menuWidth = menuFX.addRange('Image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));
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ui.menuHeight = menuFX.addRange('Image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));
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menuFX.addRange('Brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));
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menuFX.addRange('Contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));
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menuFX.addRange('Sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));
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@ -219,6 +219,7 @@ class Menu {
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evt.target.setAttribute('value', evt.target.value);
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if (callback) callback(evt.target.value);
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});
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el.input = el.children[0];
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return el;
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}
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@ -3,7 +3,7 @@ const path = require('path');
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const dayjs = require('dayjs');
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const simpleGit = require('simple-git/promise');
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const logger = require('@vladmandic/pilogger');
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const app = require('./package.json');
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const app = require('../package.json');
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const git = simpleGit();
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@ -45,5 +45,5 @@ async function update(f) {
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exports.update = update;
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if (!module.parent) {
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update('wiki/Change-Log.md');
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update('../wiki/Change-Log.md');
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}
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@ -26,9 +26,9 @@ const log = require('@vladmandic/pilogger');
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const options = {
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// key: fs.readFileSync('/home/vlado/dev/piproxy/cert/private.pem'),
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// cert: fs.readFileSync('/home/vlado/dev/piproxy/cert/fullchain.pem'),
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key: fs.readFileSync('./dev-server.key'),
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cert: fs.readFileSync('./dev-server.crt'),
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root: '.',
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key: fs.readFileSync('dev-server/dev-server.key'),
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cert: fs.readFileSync('dev-server/dev-server.crt'),
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root: '..',
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default: 'demo/index.html',
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port: 8000,
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monitor: ['package.json', 'config.js', 'demo', 'src'],
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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": 17856,
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"bytes": 18278,
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"imports": [
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{
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"path": "dist/human.esm.js"
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@ -19,11 +19,11 @@
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"imports": []
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},
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"demo/menu.js": {
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"bytes": 12676,
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"imports": []
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},
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"dist/human.esm.js": {
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"bytes": 3196462,
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"imports": []
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}
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},
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@ -31,28 +31,25 @@
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"dist/demo-browser-index.js.map": {
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"imports": [],
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"inputs": {},
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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": 1664606
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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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"demo/menu.js": {
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}
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},
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}
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}
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}
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"imports": []
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},
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"package.json": {
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"imports": []
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},
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"src/age/age.js": {
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"bytesInOutput": 1325
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"package.json": {
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},
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"src/human.js": {
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"bytesInOutput": 0
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{
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"inputs": {
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"config.js": {
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"bytes": 7664,
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"imports": []
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},
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"node_modules/@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.node.js": {
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]
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"imports": []
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},
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"src/age/age.js": {
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"imports": []
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},
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"src/hand/box.js": {
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"imports": [
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{
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"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
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]
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},
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"src/hand/handdetector.js": {
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"bytes": 4220,
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"imports": [
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{
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"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
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@ -398,7 +398,7 @@
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]
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},
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"src/hand/handpipeline.js": {
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"imports": [
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{
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"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
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]
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},
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"src/hand/handpose.js": {
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"bytes": 3095,
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"imports": [
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{
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"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
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"dist/human.esm.js.map": {
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"imports": [],
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"inputs": {},
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},
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"dist/human.esm.js": {
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"imports": [],
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"inputs": {
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"src/body/keypoints.js": {
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"bytesInOutput": 1633
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},
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"src/body/vectors.js": {
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"bytesInOutput": 616
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"src/body/decodePose.js": {
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"src/body/util.js": {
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"src/gesture.js": {
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"package.json": {
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"bytesInOutput": 1324
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"package.json": {
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"bytesInOutput": 7201
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"bytes": 216537
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}
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}
|
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|
|
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|||
|
|
@ -41,16 +41,16 @@
|
|||
"scripts": {
|
||||
"start": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",
|
||||
"lint": "eslint src/*.js demo/*.js",
|
||||
"dev": "npm install && node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation dev-server.js",
|
||||
"dev": "npm install && node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation dev-server/dev-server.js",
|
||||
"changelog": "node dev-server/changelog.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",
|
||||
"build-node": "esbuild --bundle --minify --platform=node --sourcemap --target=es2018 --format=cjs --metafile=dist/human.node.json --outfile=dist/human.node.js src/human.js",
|
||||
"build-node-nobundle": "esbuild --bundle --minify --platform=node --sourcemap --target=es2018 --format=cjs --external:@tensorflow --metafile=dist/human.node.json --outfile=dist/human.node-nobundle.js src/human.js",
|
||||
"build-demo": "esbuild --bundle --log-level=error --platform=browser --sourcemap --target=es2018 --format=esm --external:fs --metafile=dist/demo-browser-index.json --outfile=dist/demo-browser-index.js demo/browser.js",
|
||||
"build": "rimraf dist/* && npm run build-iife && npm run build-esm-bundle && npm run build-esm-nobundle && npm run build-node && npm run build-node-nobundle && npm run build-demo",
|
||||
"update": "npm update --depth 20 --force && npm dedupe && npm prune && npm audit",
|
||||
"changelog": "node changelog.js"
|
||||
"build": "rimraf dist/* && npm run build-iife && npm run build-esm-bundle && npm run build-esm-nobundle && npm run build-node && npm run build-node-nobundle && npm run build-demo && npm run changelog",
|
||||
"update": "npm update --depth 20 --force && npm dedupe && npm prune && npm audit"
|
||||
},
|
||||
"keywords": [
|
||||
"tensorflowjs",
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ function scaleBoxCoordinates(box, factor) {
|
|||
const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
|
||||
return scaledCoord;
|
||||
});
|
||||
return { startPoint, endPoint, palmLandmarks };
|
||||
return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
|
||||
}
|
||||
function enlargeBox(box, factor = 1.5) {
|
||||
const center = getBoxCenter(box);
|
||||
|
|
|
|||
|
|
@ -49,29 +49,28 @@ class HandDetector {
|
|||
async getBoxes(input, config) {
|
||||
const batched = this.model.predict(input);
|
||||
const predictions = batched.squeeze();
|
||||
batched.dispose();
|
||||
const scores = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());
|
||||
// const scoresVal = scores.dataSync(); // scoresVal[boxIndex] is box confidence
|
||||
const scoresVal = scores.dataSync();
|
||||
const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);
|
||||
const boxes = this.normalizeBoxes(rawBoxes);
|
||||
const boxesWithHandsT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.maxHands, config.iouThreshold, config.scoreThreshold);
|
||||
const boxesWithHands = boxesWithHandsT.arraySync();
|
||||
const toDispose = [
|
||||
batched,
|
||||
boxesWithHandsT,
|
||||
predictions,
|
||||
boxes,
|
||||
rawBoxes,
|
||||
scores,
|
||||
];
|
||||
rawBoxes.dispose();
|
||||
const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.maxHands, config.iouThreshold, config.scoreThreshold);
|
||||
const filtered = filteredT.arraySync();
|
||||
scores.dispose();
|
||||
filteredT.dispose();
|
||||
const hands = [];
|
||||
for (const boxIndex of boxesWithHands) {
|
||||
for (const boxIndex of filtered) {
|
||||
if (scoresVal[boxIndex] >= config.minConfidence) {
|
||||
const matchingBox = tf.slice(boxes, [boxIndex, 0], [1, -1]);
|
||||
const rawPalmLandmarks = tf.slice(predictions, [boxIndex, 5], [1, 14]);
|
||||
const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, boxIndex).reshape([-1, 2]));
|
||||
rawPalmLandmarks.dispose();
|
||||
hands.push({ box: matchingBox, palmLandmarks });
|
||||
hands.push({ box: matchingBox, palmLandmarks, confidence: scoresVal[boxIndex] });
|
||||
}
|
||||
toDispose.forEach((tensor) => tensor.dispose());
|
||||
}
|
||||
predictions.dispose();
|
||||
boxes.dispose();
|
||||
return hands;
|
||||
}
|
||||
|
||||
|
|
@ -84,13 +83,13 @@ class HandDetector {
|
|||
if (!predictions || predictions.length === 0) return null;
|
||||
const hands = [];
|
||||
for (const prediction of predictions) {
|
||||
const boundingBoxes = prediction.box.dataSync();
|
||||
const startPoint = boundingBoxes.slice(0, 2);
|
||||
const endPoint = boundingBoxes.slice(2, 4);
|
||||
const boxes = prediction.box.dataSync();
|
||||
const startPoint = boxes.slice(0, 2);
|
||||
const endPoint = boxes.slice(2, 4);
|
||||
const palmLandmarks = prediction.palmLandmarks.arraySync();
|
||||
prediction.box.dispose();
|
||||
prediction.palmLandmarks.dispose();
|
||||
hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks }, [inputWidth / config.inputSize, inputHeight / config.inputSize]));
|
||||
hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.inputSize, inputHeight / config.inputSize]));
|
||||
}
|
||||
return hands;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -19,7 +19,6 @@ const tf = require('@tensorflow/tfjs');
|
|||
const box = require('./box');
|
||||
const util = require('./util');
|
||||
|
||||
const UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.8;
|
||||
const PALM_BOX_SHIFT_VECTOR = [0, -0.4];
|
||||
const PALM_BOX_ENLARGE_FACTOR = 3;
|
||||
const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection
|
||||
|
|
@ -87,23 +86,29 @@ class HandPipeline {
|
|||
async estimateHands(image, config) {
|
||||
this.skipped++;
|
||||
let useFreshBox = false;
|
||||
// run new detector every skipFrames
|
||||
const boxes = (this.skipped > config.skipFrames)
|
||||
? await this.boxDetector.estimateHandBounds(image, config) : null;
|
||||
|
||||
// run new detector every skipFrames unless we only want box to start with
|
||||
let boxes;
|
||||
if ((this.skipped > config.skipFrames) || !config.landmarks) {
|
||||
boxes = await this.boxDetector.estimateHandBounds(image, config);
|
||||
this.skipped = 0;
|
||||
}
|
||||
|
||||
// if detector result count doesn't match current working set, use it to reset current working set
|
||||
if (boxes && (boxes.length !== this.detectedHands) && (this.detectedHands !== config.maxHands)) {
|
||||
// console.log(this.skipped, config.maxHands, this.detectedHands, this.storedBoxes.length, boxes.length);
|
||||
if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.maxHands) || !config.landmarks)) {
|
||||
this.storedBoxes = [];
|
||||
this.detectedHands = 0;
|
||||
for (const possible of boxes) this.storedBoxes.push(possible);
|
||||
if (this.storedBoxes.length > 0) useFreshBox = true;
|
||||
this.skipped = 0;
|
||||
}
|
||||
const hands = [];
|
||||
// console.log(`skipped: ${this.skipped} max: ${config.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);
|
||||
|
||||
// go through working set of boxes
|
||||
for (const i in this.storedBoxes) {
|
||||
const currentBox = this.storedBoxes[i];
|
||||
if (!currentBox) continue;
|
||||
if (config.landmarks) {
|
||||
const angle = util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]);
|
||||
const palmCenter = box.getBoxCenter(currentBox);
|
||||
const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];
|
||||
|
|
@ -125,30 +130,31 @@ class HandPipeline {
|
|||
keypointsReshaped.dispose();
|
||||
const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
|
||||
const nextBoundingBox = this.getBoxForHandLandmarks(coords);
|
||||
this.updateStoredBoxes(nextBoundingBox, i);
|
||||
this.storedBoxes[i] = nextBoundingBox;
|
||||
const result = {
|
||||
landmarks: coords,
|
||||
handInViewConfidence: confidenceValue,
|
||||
boundingBox: {
|
||||
confidence: confidenceValue,
|
||||
box: {
|
||||
topLeft: nextBoundingBox.startPoint,
|
||||
bottomRight: nextBoundingBox.endPoint,
|
||||
},
|
||||
};
|
||||
hands.push(result);
|
||||
} else {
|
||||
this.updateStoredBoxes(null, i);
|
||||
/*
|
||||
this.storedBoxes[i] = null;
|
||||
}
|
||||
keypoints.dispose();
|
||||
} else {
|
||||
const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);
|
||||
const result = {
|
||||
handInViewConfidence: confidenceValue,
|
||||
boundingBox: {
|
||||
topLeft: currentBox.startPoint,
|
||||
bottomRight: currentBox.endPoint,
|
||||
confidence: currentBox.confidence,
|
||||
box: {
|
||||
topLeft: enlarged.startPoint,
|
||||
bottomRight: enlarged.endPoint,
|
||||
},
|
||||
};
|
||||
hands.push(result);
|
||||
*/
|
||||
}
|
||||
keypoints.dispose();
|
||||
}
|
||||
this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
|
||||
this.detectedHands = hands.length;
|
||||
|
|
@ -163,26 +169,6 @@ class HandPipeline {
|
|||
const endPoint = [Math.max(...xs), Math.max(...ys)];
|
||||
return { startPoint, endPoint };
|
||||
}
|
||||
|
||||
updateStoredBoxes(newBox, i) {
|
||||
const previousBox = this.storedBoxes[i];
|
||||
let iou = 0;
|
||||
if (newBox && previousBox && previousBox.startPoint) {
|
||||
const [boxStartX, boxStartY] = newBox.startPoint;
|
||||
const [boxEndX, boxEndY] = newBox.endPoint;
|
||||
const [previousBoxStartX, previousBoxStartY] = previousBox.startPoint;
|
||||
const [previousBoxEndX, previousBoxEndY] = previousBox.endPoint;
|
||||
const xStartMax = Math.max(boxStartX, previousBoxStartX);
|
||||
const yStartMax = Math.max(boxStartY, previousBoxStartY);
|
||||
const xEndMin = Math.min(boxEndX, previousBoxEndX);
|
||||
const yEndMin = Math.min(boxEndY, previousBoxEndY);
|
||||
const intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax);
|
||||
const boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY);
|
||||
const previousBoxArea = (previousBoxEndX - previousBoxStartX) * (previousBoxEndY - boxStartY);
|
||||
iou = intersection / (boxArea + previousBoxArea - intersection);
|
||||
}
|
||||
this.storedBoxes[i] = iou > UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD ? previousBox : newBox;
|
||||
}
|
||||
}
|
||||
|
||||
exports.HandPipeline = HandPipeline;
|
||||
|
|
|
|||
|
|
@ -51,12 +51,12 @@ class HandPose {
|
|||
}
|
||||
}
|
||||
hands.push({
|
||||
confidence: prediction.handInViewConfidence,
|
||||
box: prediction.boundingBox ? [
|
||||
prediction.boundingBox.topLeft[0],
|
||||
prediction.boundingBox.topLeft[1],
|
||||
prediction.boundingBox.bottomRight[0] - prediction.boundingBox.topLeft[0],
|
||||
prediction.boundingBox.bottomRight[1] - prediction.boundingBox.topLeft[1],
|
||||
confidence: prediction.confidence,
|
||||
box: prediction.box ? [
|
||||
prediction.box.topLeft[0],
|
||||
prediction.box.topLeft[1],
|
||||
prediction.box.bottomRight[0] - prediction.box.topLeft[0],
|
||||
prediction.box.bottomRight[1] - prediction.box.topLeft[1],
|
||||
] : 0,
|
||||
landmarks: prediction.landmarks,
|
||||
annotations,
|
||||
|
|
|
|||
Loading…
Reference in New Issue