mirror of https://github.com/vladmandic/human
updated emotion models
Vladimir Mandic
parent
8ae7b8b47b
commit
09756545cc
14
config.js
14
config.js
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@ -56,7 +56,7 @@ export default {
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skipFrames: 10, // how many frames to go without re-running the face bounding box detector, only used for video inputs
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// if model is running st 25 FPS, we can re-use existing bounding box for updated face mesh analysis
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// as face probably hasn't moved much in short time (10 * 1/25 = 0.25 sec)
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minConfidence: 0.5, // threshold for discarding a prediction
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minConfidence: 0.3, // threshold for discarding a prediction
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iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression
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scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score in non-maximum suppression
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},
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@ -74,21 +74,21 @@ export default {
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age: {
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enabled: true,
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modelPath: '../models/ssrnet-age-imdb.json', // can be 'imdb' or 'wiki'
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// which determines training set for model
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// which determines training set for model
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inputSize: 64, // fixed value
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skipFrames: 10, // how many frames to go without re-running the detector, only used for video inputs
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},
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gender: {
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enabled: true,
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minConfidence: 0.8, // threshold for discarding a prediction
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minConfidence: 0.3, // threshold for discarding a prediction
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modelPath: '../models/ssrnet-gender-imdb.json',
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},
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emotion: {
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enabled: true,
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inputSize: 64, // fixed value
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minConfidence: 0.5, // threshold for discarding a prediction
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inputSize: 64, // fixed value, 64 for 'mini' and 'lage', 48 for 'cnn'
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minConfidence: 0.3, // threshold for discarding a prediction
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skipFrames: 10, // how many frames to go without re-running the detector
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modelPath: '../models/emotion.json',
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modelPath: '../models/emotion-large.json', // can be 'mini', 'large' or 'cnn'
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},
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},
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body: {
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@ -106,7 +106,7 @@ export default {
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skipFrames: 10, // how many frames to go without re-running the hand bounding box detector, only used for video inputs
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// if model is running st 25 FPS, we can re-use existing bounding box for updated hand skeleton analysis
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// as the hand probably hasn't moved much in short time (10 * 1/25 = 0.25 sec)
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minConfidence: 0.5, // threshold for discarding a prediction
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minConfidence: 0.3, // threshold for discarding a prediction
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iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression
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scoreThreshold: 0.5, // 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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@ -10,7 +10,7 @@ const ui = {
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baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'
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baseLabel: 'rgba(173, 216, 230, 0.9)', // 'lightblue' with dark alpha channel
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baseFontProto: 'small-caps {size} "Segoe UI"',
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baseLineWidth: 16,
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baseLineWidth: 12,
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baseLineHeightProto: 2,
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columns: 2,
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busy: false,
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@ -55,15 +55,15 @@ const config = {
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videoOptimized: true,
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face: {
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enabled: true,
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detector: { maxFaces: 10, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.5 },
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detector: { maxFaces: 10, skipFrames: 10, minConfidence: 0.3, iouThreshold: 0.3, scoreThreshold: 0.5 },
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mesh: { enabled: true },
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iris: { enabled: true },
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age: { enabled: true, skipFrames: 10 },
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gender: { enabled: true },
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emotion: { enabled: true, minConfidence: 0.5, useGrayscale: true },
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emotion: { enabled: true, minConfidence: 0.3, useGrayscale: true },
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},
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body: { enabled: true, maxDetections: 10, scoreThreshold: 0.5, nmsRadius: 20 },
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hand: { enabled: true, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.5 },
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hand: { enabled: true, skipFrames: 10, minConfidence: 0.3, iouThreshold: 0.3, scoreThreshold: 0.5 },
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gesture: { enabled: true },
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};
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@ -149,7 +149,7 @@ async function setupCamera() {
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const output = document.getElementById('log');
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const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;
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let msg = '';
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status('starting camera');
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status('setting up camera');
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// setup webcam. note that navigator.mediaDevices requires that page is accessed via https
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if (!navigator.mediaDevices) {
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msg = 'camera access not supported';
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@ -179,9 +179,7 @@ async function setupCamera() {
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else return null;
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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 });
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log('camera settings:', settings);
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log('camera track:', track);
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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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return new Promise((resolve) => {
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video.onloadeddata = async () => {
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@ -193,6 +191,7 @@ async function setupCamera() {
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ui.busy = false;
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// do once more because onresize events can be delayed or skipped
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// if (video.width > window.innerWidth) await setupCamera();
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status('');
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resolve(video);
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};
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});
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@ -222,32 +221,29 @@ function webWorker(input, image, canvas) {
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// main processing function when input is webcam, can use direct invocation or web worker
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function runHumanDetect(input, canvas) {
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timeStamp = performance.now();
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// perform detect if live video or not video at all
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if (input.srcObject) {
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// if video not ready, just redo
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const live = (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);
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if (!live) {
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if (!input.paused) {
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log(`video not ready: state: ${input.srcObject.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);
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setTimeout(() => runHumanDetect(input, canvas), 500);
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}
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return;
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}
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if (ui.useWorker) {
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// get image data from video as we cannot send html objects to webworker
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const offscreen = new OffscreenCanvas(canvas.width, canvas.height);
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const ctx = offscreen.getContext('2d');
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ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);
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const data = ctx.getImageData(0, 0, canvas.width, canvas.height);
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// perform detection in worker
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webWorker(input, data, canvas);
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} else {
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human.detect(input, config).then((result) => {
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if (result.error) log(result.error);
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else drawResults(input, result, canvas);
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if (config.profile) log('profile data:', human.profile());
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});
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}
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// if live video
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const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);
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if (!live) {
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// if we want to continue and camera not ready, retry in 0.5sec, else just give up
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if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);
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else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);
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return;
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}
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status('');
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if (ui.useWorker) {
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// get image data from video as we cannot send html objects to webworker
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const offscreen = new OffscreenCanvas(canvas.width, canvas.height);
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const ctx = offscreen.getContext('2d');
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ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);
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const data = ctx.getImageData(0, 0, canvas.width, canvas.height);
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// perform detection in worker
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webWorker(input, data, canvas);
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} else {
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human.detect(input, config).then((result) => {
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if (result.error) log(result.error);
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else drawResults(input, result, canvas);
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if (config.profile) log('profile data:', human.profile());
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});
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}
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}
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@ -286,7 +282,8 @@ async function detectVideo() {
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document.getElementById('canvas').style.display = 'block';
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const video = document.getElementById('video');
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const canvas = document.getElementById('canvas');
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ui.baseFont = ui.baseFontProto.replace(/{size}/, '1.3rem');
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const size = 12 + Math.trunc(window.innerWidth / 400);
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ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);
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ui.baseLineHeight = ui.baseLineHeightProto;
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if ((video.srcObject !== null) && !video.paused) {
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document.getElementById('play').style.display = 'block';
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@ -305,7 +302,8 @@ async function detectVideo() {
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async function detectSampleImages() {
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document.getElementById('play').style.display = 'none';
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config.videoOptimized = false;
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ui.baseFont = ui.baseFontProto.replace(/{size}/, `${1.3 * ui.columns}rem`);
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const size = Math.trunc(ui.columns * 25600 / window.innerWidth);
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ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);
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ui.baseLineHeight = ui.baseLineHeightProto * ui.columns;
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document.getElementById('canvas').style.display = 'none';
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document.getElementById('samples-container').style.display = 'block';
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@ -30,7 +30,10 @@ async function drawFace(result, canvas, ui, triangulation) {
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if (face.agConfidence) labels.push(`${Math.trunc(100 * face.agConfidence)}% ${face.gender || ''}`);
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if (face.age) labels.push(`age: ${face.age || ''}`);
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if (face.iris) labels.push(`iris: ${face.iris}`);
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if (face.emotion && face.emotion[0]) labels.push(`${Math.trunc(100 * face.emotion[0].score)}% ${face.emotion[0].emotion}`);
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if (face.emotion && face.emotion.length > 0) {
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const emotion = face.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
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labels.push(emotion.join(' '));
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}
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ctx.fillStyle = ui.baseLabel;
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for (const i in labels) ctx.fillText(labels[i], face.box[0] + 8, face.box[1] + 24 + ((i + 1) * ui.baseLineHeight));
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ctx.stroke();
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@ -35,7 +35,7 @@
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.log { position: fixed; bottom: 0; margin: 0.4rem; font-size: 0.9rem; }
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.samples-container { display: flex; flex-wrap: wrap; }
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.video { display: none; }
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.canvas { margin: 0 auto; height: 100%; }
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.canvas { margin: 0 auto; height: 100vh; }
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.loader { width: 300px; height: 300px; border: 3px solid transparent; border-radius: 50%; border-top: 4px solid #f15e41; animation: spin 4s linear infinite; position: absolute; top: 30%; left: 50%; margin-left: -150px; z-index: 15; }
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.loader::before, .loader::after { content: ""; position: absolute; top: 6px; bottom: 6px; left: 6px; right: 6px; border-radius: 50%; border: 4px solid transparent; }
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.loader::before { border-top-color: #bad375; animation: 3s spin linear infinite; }
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{
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"imports": [
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"path": "dist/human.esm.js"
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]
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"imports": []
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},
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"src/emotion/emotion.js": {
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"path": "src/profile.js"
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|
||||
"weights": [{"name":"unknown_60","shape":[3,3,64,1],"dtype":"float32"},{"name":"unknown_66","shape":[3,3,128,1],"dtype":"float32"},{"name":"unknown_43","shape":[3,3,32,1],"dtype":"float32"},{"name":"unknown_49","shape":[3,3,64,1],"dtype":"float32"},{"name":"unknown_26","shape":[3,3,16,1],"dtype":"float32"},{"name":"unknown_32","shape":[3,3,32,1],"dtype":"float32"},{"name":"unknown_9","shape":[3,3,8,1],"dtype":"float32"},{"name":"unknown_15","shape":[3,3,16,1],"dtype":"float32"},{"name":"unknown_77","shape":[3,3,128,7],"dtype":"float32"},{"name":"unknown_78","shape":[7],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/global_average_pooling2d_1/Mean/reduction_indices","shape":[2],"dtype":"int32"},{"name":"StatefulPartitionedCall/model_1/conv2d_1/Conv2D_weights","shape":[3,3,1,8],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_6/Conv2D_weights","shape":[1,1,64,128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_1/Conv2D_bn_offset","shape":[8],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_2/Conv2D_weights","shape":[3,3,8,8],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_6/Conv2D_bn_offset","shape":[128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_2/Conv2D_bn_offset","shape":[8],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_3/Conv2D_weights","shape":[1,1,8,16],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_7/separable_conv2d_weights","shape":[1,1,64,128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_3/Conv2D_bn_offset","shape":[16],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_1/separable_conv2d_weights","shape":[1,1,8,16],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_1/separable_conv2d_bn_offset","shape":[16],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_7/separable_conv2d_bn_offset","shape":[128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_2/separable_conv2d_weights","shape":[1,1,16,16],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_2/separable_conv2d_bn_offset","shape":[16],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_4/Conv2D_weights","shape":[1,1,16,32],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_4/Conv2D_bn_offset","shape":[32],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_3/separable_conv2d_weights","shape":[1,1,16,32],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_3/separable_conv2d_bn_offset","shape":[32],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_8/separable_conv2d_weights","shape":[1,1,128,128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_4/separable_conv2d_weights","shape":[1,1,32,32],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_4/separable_conv2d_bn_offset","shape":[32],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_5/Conv2D_weights","shape":[1,1,32,64],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_8/separable_conv2d_bn_offset","shape":[128],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/conv2d_5/Conv2D_bn_offset","shape":[64],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_5/separable_conv2d_weights","shape":[1,1,32,64],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_5/separable_conv2d_bn_offset","shape":[64],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_6/separable_conv2d_weights","shape":[1,1,64,64],"dtype":"float32"},{"name":"StatefulPartitionedCall/model_1/separable_conv2d_6/separable_conv2d_bn_offset","shape":[64],"dtype":"float32"}]
|
||||
}
|
||||
]
|
||||
|
|
@ -5,7 +5,11 @@ const annotations = ['angry', 'discust', 'fear', 'happy', 'sad', 'surpise', 'neu
|
|||
const models = {};
|
||||
let last = [];
|
||||
let frame = Number.MAX_SAFE_INTEGER;
|
||||
const multiplier = 1.5;
|
||||
|
||||
// tuning values
|
||||
const zoom = [0, 0]; // 0..1 meaning 0%..100%
|
||||
const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
|
||||
const scale = 1; // score multiplication factor
|
||||
|
||||
async function load(config) {
|
||||
if (!models.emotion) models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);
|
||||
|
|
@ -18,17 +22,25 @@ async function predict(image, config) {
|
|||
return last;
|
||||
}
|
||||
frame = 0;
|
||||
const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);
|
||||
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, [0.2989]);
|
||||
const greenNorm = tf.mul(green, [0.5870]);
|
||||
const blueNorm = tf.mul(blue, [0.1140]);
|
||||
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();
|
||||
|
|
@ -36,7 +48,7 @@ async function predict(image, config) {
|
|||
if (config.face.emotion.enabled) {
|
||||
let data;
|
||||
if (!config.profile) {
|
||||
const emotionT = await models.emotion.predict(grayscale);
|
||||
const emotionT = await models.emotion.predict(normalize);
|
||||
data = emotionT.dataSync();
|
||||
tf.dispose(emotionT);
|
||||
} else {
|
||||
|
|
@ -46,7 +58,7 @@ async function predict(image, config) {
|
|||
profile.run('emotion', profileData);
|
||||
}
|
||||
for (let i = 0; i < data.length; i++) {
|
||||
if (multiplier * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * multiplier * data[i]) / 100), emotion: annotations[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);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -5,6 +5,9 @@ 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;
|
||||
|
|
@ -21,7 +24,14 @@ async function predict(image, config) {
|
|||
return last;
|
||||
}
|
||||
frame = 0;
|
||||
const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
|
||||
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);
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue