feat(face-landmarks): integrate human library
It replaces face-api. * feat(face-landmarks): integrate human library * feat(face-landmarks): rewrite worker in typescript * fix(face-landmarks): allow worker bundle size up to 2 mib * fix: remove unwanted comment * code review
This commit is contained in:
Gabriel Borlea
GitHub
parent
adef5095da
commit
0c021868b5
10
Makefile
10
Makefile
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@ -7,7 +7,7 @@ TF_WASM_DIR = node_modules/@tensorflow/tfjs-backend-wasm/dist/
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RNNOISE_WASM_DIR = node_modules/rnnoise-wasm/dist
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TFLITE_WASM = react/features/stream-effects/virtual-background/vendor/tflite
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MEET_MODELS_DIR = react/features/stream-effects/virtual-background/vendor/models
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FACE_MODELS_DIR = node_modules/@vladmandic/face-api/model
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FACE_MODELS_DIR = node_modules/@vladmandic/human-models/models
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NODE_SASS = ./node_modules/.bin/sass
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NPM = npm
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OUTPUT_DIR = .
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@ -91,10 +91,10 @@ deploy-meet-models:
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deploy-face-landmarks:
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cp \
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$(FACE_MODELS_DIR)/tiny_face_detector_model-weights_manifest.json \
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$(FACE_MODELS_DIR)/tiny_face_detector_model.bin \
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$(FACE_MODELS_DIR)/face_expression_model-weights_manifest.json \
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$(FACE_MODELS_DIR)/face_expression_model.bin \
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$(FACE_MODELS_DIR)/blazeface-front.bin \
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$(FACE_MODELS_DIR)/blazeface-front.json \
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$(FACE_MODELS_DIR)/emotion.bin \
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$(FACE_MODELS_DIR)/emotion.json \
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$(DEPLOY_DIR)
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deploy-css:
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@ -53,7 +53,8 @@
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"@svgr/webpack": "4.3.2",
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"@tensorflow/tfjs-backend-wasm": "3.13.0",
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"@tensorflow/tfjs-core": "3.13.0",
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"@vladmandic/face-api": "1.6.4",
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"@vladmandic/human": "2.6.5",
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"@vladmandic/human-models": "2.5.9",
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"@xmldom/xmldom": "0.7.5",
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"amplitude-js": "8.2.1",
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"base64-js": "1.3.1",
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@ -5587,14 +5588,22 @@
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"resolved": "https://registry.npmjs.org/@types/yargs-parser/-/yargs-parser-20.2.1.tgz",
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"integrity": "sha512-7tFImggNeNBVMsn0vLrpn1H1uPrUBdnARPTpZoitY37ZrdJREzf7I16tMrlK3hen349gr1NYh8CmZQa7CTG6Aw=="
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},
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"node_modules/@vladmandic/face-api": {
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"version": "1.6.4",
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"resolved": "https://registry.npmjs.org/@vladmandic/face-api/-/face-api-1.6.4.tgz",
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"integrity": "sha512-tVx8lCL1mKb44qeN5EEypJNXqxRYXh+7BcSzfY4iMaZIoF5Y+Jev20UiIn9JvxwGV2caWkdFIjpvw+OxsL/kdg==",
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"node_modules/@vladmandic/human": {
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"version": "2.6.5",
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"resolved": "https://registry.npmjs.org/@vladmandic/human/-/human-2.6.5.tgz",
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"integrity": "sha512-5fG5lICkJw17d8fT9ZHtioAPLmOKsQlNN4rbuA1t21HlV7KL3M2EMbYeO+ZE0t6VLSioZZH/KoOFvW5A0JP0Hg==",
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"engines": {
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"node": ">=14.0.0"
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}
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},
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"node_modules/@vladmandic/human-models": {
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"version": "2.5.9",
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"resolved": "https://registry.npmjs.org/@vladmandic/human-models/-/human-models-2.5.9.tgz",
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"integrity": "sha512-WhV0RIeELsA73LGMpemYJvpIEuG0nkh6rP1x7JctKDQkNaARgWolTq9r1RUvWw++rQfzfZ82bYcAauJcaGW6bw==",
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"dependencies": {
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"@vladmandic/human": "^2.5.8"
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}
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},
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"node_modules/@webassemblyjs/ast": {
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"version": "1.11.1",
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"resolved": "https://registry.npmjs.org/@webassemblyjs/ast/-/ast-1.11.1.tgz",
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@ -24180,10 +24189,18 @@
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"resolved": "https://registry.npmjs.org/@types/yargs-parser/-/yargs-parser-20.2.1.tgz",
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"integrity": "sha512-7tFImggNeNBVMsn0vLrpn1H1uPrUBdnARPTpZoitY37ZrdJREzf7I16tMrlK3hen349gr1NYh8CmZQa7CTG6Aw=="
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},
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"@vladmandic/face-api": {
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"version": "1.6.4",
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"resolved": "https://registry.npmjs.org/@vladmandic/face-api/-/face-api-1.6.4.tgz",
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"integrity": "sha512-tVx8lCL1mKb44qeN5EEypJNXqxRYXh+7BcSzfY4iMaZIoF5Y+Jev20UiIn9JvxwGV2caWkdFIjpvw+OxsL/kdg=="
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"@vladmandic/human": {
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"version": "2.6.5",
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"resolved": "https://registry.npmjs.org/@vladmandic/human/-/human-2.6.5.tgz",
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"integrity": "sha512-5fG5lICkJw17d8fT9ZHtioAPLmOKsQlNN4rbuA1t21HlV7KL3M2EMbYeO+ZE0t6VLSioZZH/KoOFvW5A0JP0Hg=="
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},
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"@vladmandic/human-models": {
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"version": "2.5.9",
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"resolved": "https://registry.npmjs.org/@vladmandic/human-models/-/human-models-2.5.9.tgz",
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"integrity": "sha512-WhV0RIeELsA73LGMpemYJvpIEuG0nkh6rP1x7JctKDQkNaARgWolTq9r1RUvWw++rQfzfZ82bYcAauJcaGW6bw==",
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"requires": {
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"@vladmandic/human": "^2.5.8"
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}
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},
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"@webassemblyjs/ast": {
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"version": "1.11.1",
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@ -58,7 +58,8 @@
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"@svgr/webpack": "4.3.2",
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"@tensorflow/tfjs-backend-wasm": "3.13.0",
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"@tensorflow/tfjs-core": "3.13.0",
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"@vladmandic/face-api": "1.6.4",
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"@vladmandic/human": "2.6.5",
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"@vladmandic/human-models": "2.5.9",
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"@xmldom/xmldom": "0.7.5",
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"amplitude-js": "8.2.1",
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"base64-js": "1.3.1",
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@ -1,5 +1,3 @@
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// @flow
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export const FACE_EXPRESSIONS_EMOJIS = {
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happy: '😊',
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neutral: '😐',
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@ -13,6 +11,16 @@ export const FACE_EXPRESSIONS_EMOJIS = {
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export const FACE_EXPRESSIONS = [ 'happy', 'neutral', 'sad', 'surprised', 'angry', 'fearful' ];
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export const FACE_EXPRESSIONS_NAMING_MAPPING = {
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happy: 'happy',
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neutral: 'neutral',
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surprise: 'surprised',
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angry: 'angry',
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fear: 'fearful',
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disgust: 'disgusted',
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sad: 'sad'
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};
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/**
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* Time is ms used for sending expression.
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*/
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@ -1,108 +0,0 @@
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/* eslint-disable */
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// From: https://github.com/justadudewhohacks/face-api.js/issues/47
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// This is needed because face-api.js does not support working in a WebWorker natively
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// Updated Dec 1 2020 to work on latest Chrome (tested in WebWorkers on Chrome Mobile on Android / Google Pixel 3 as well)
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self.useWasm = false;
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if(!self.OffscreenCanvas) {
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self.useWasm = true;
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self.OffscreenCanvas = class OffscreenCanvas {
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constructor() {
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}
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}
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}
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if(!self.OffscreenCanvasRenderingContext2D) {
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self.OffscreenCanvasRenderingContext2D = class OffscreenCanvasRenderingContext2D {
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constructor() {
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}
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}
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}
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self.Canvas = self.HTMLCanvasElement = OffscreenCanvas;
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// self.HTMLCanvasElement.name = 'HTMLCanvasElement';
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// self.Canvas.name = 'Canvas';
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self.CanvasRenderingContext2D = OffscreenCanvasRenderingContext2D;
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function HTMLImageElement(){}
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function HTMLVideoElement(){}
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self.Image = HTMLImageElement;
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self.Video = HTMLVideoElement;
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function Storage () {
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let _data = {};
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this.clear = function(){ return _data = {}; };
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this.getItem = function(id){ return _data.hasOwnProperty(id) ? _data[id] : undefined; };
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this.removeItem = function(id){ return delete _data[id]; };
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this.setItem = function(id, val){ return _data[id] = String(val); };
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}
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class Document extends EventTarget {}
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self.document = new Document();
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self.window = self.Window = self;
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self.localStorage = new Storage();
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function createElement(element) {
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switch(element) {
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case 'canvas':
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let canvas = new Canvas(1,1);
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canvas.localName = 'canvas';
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canvas.nodeName = 'CANVAS';
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canvas.tagName = 'CANVAS';
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canvas.nodeType = 1;
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canvas.innerHTML = '';
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canvas.remove = () => { console.log('nope'); };
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return canvas;
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default:
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console.log('arg', element);
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break;
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}
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}
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document.createElement = createElement;
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document.location = self.location;
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// These are the same checks face-api.js/isBrowser does
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if(!typeof window == 'object') {
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console.warn("Check failed: window");
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}
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if(typeof document === 'undefined') {
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console.warn("Check failed: document");
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}
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if(typeof HTMLImageElement === 'undefined') {
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console.warn("Check failed: HTMLImageElement");
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}
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if(typeof HTMLCanvasElement === 'undefined') {
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console.warn("Check failed: HTMLCanvasElement");
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}
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if(typeof HTMLVideoElement === 'undefined') {
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console.warn("Check failed: HTMLVideoElement");
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}
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if(typeof ImageData === 'undefined') {
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console.warn("Check failed: ImageData");
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}
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if(typeof CanvasRenderingContext2D === 'undefined') {
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console.warn("Check failed: CanvasRenderingContext2D");
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}
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self.window = window;
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self.document = document;
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self.HTMLImageElement = HTMLImageElement;
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self.HTMLVideoElement = HTMLVideoElement;
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// These are the same checks face-api.js/isBrowser does
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const isBrowserCheck = typeof window === 'object'
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&& typeof document !== 'undefined'
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&& typeof HTMLImageElement !== 'undefined'
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&& typeof HTMLCanvasElement !== 'undefined'
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&& typeof HTMLVideoElement !== 'undefined'
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&& typeof ImageData !== 'undefined'
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&& typeof CanvasRenderingContext2D !== 'undefined';
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;
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if(!isBrowserCheck) {
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throw new Error("Failed to monkey patch for face-api, face-api will fail");
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}
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@ -1,159 +0,0 @@
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import './faceApiPatch';
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import { setWasmPaths } from '@tensorflow/tfjs-backend-wasm';
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import * as faceapi from '@vladmandic/face-api';
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import { DETECTION_TYPES, DETECT_FACE, INIT_WORKER } from './constants';
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/**
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* Detection types to be applied.
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*/
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let faceDetectionTypes = [];
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/**
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* Indicates whether an init error occured.
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*/
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let initError = false;
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/**
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* A flag that indicates whether the models are loaded or not.
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*/
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let modelsLoaded = false;
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/**
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* A flag that indicates whether the tensorflow backend is set or not.
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*/
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let backendSet = false;
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/**
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* Flag for indicating whether a face detection flow is in progress or not.
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*/
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let detectionInProgress = false;
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/**
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* Contains the last valid face bounding box (passes threshold validation) which was sent to the main process.
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*/
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let lastValidFaceBox;
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const detectFaceBox = async ({ detections, threshold }) => {
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if (!detections.length) {
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return null;
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}
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const faceBox = {
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// normalize to percentage based
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left: Math.round(Math.min(...detections.map(d => d.relativeBox.left)) * 100),
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right: Math.round(Math.max(...detections.map(d => d.relativeBox.right)) * 100)
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};
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faceBox.width = Math.round(faceBox.right - faceBox.left);
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if (lastValidFaceBox && Math.abs(lastValidFaceBox.left - faceBox.left) < threshold) {
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return null;
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}
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lastValidFaceBox = faceBox;
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return faceBox;
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};
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const detectFaceExpression = async ({ detections }) =>
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detections[0]?.expressions.asSortedArray()[0].expression;
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const detect = async ({ image, threshold }) => {
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let detections;
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let faceExpression;
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let faceBox;
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detectionInProgress = true;
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faceapi.tf.engine().startScope();
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const imageTensor = faceapi.tf.browser.fromPixels(image);
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if (faceDetectionTypes.includes(DETECTION_TYPES.FACE_EXPRESSIONS)) {
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detections = await faceapi.detectAllFaces(
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imageTensor,
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new faceapi.TinyFaceDetectorOptions()
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).withFaceExpressions();
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faceExpression = await detectFaceExpression({ detections });
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}
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if (faceDetectionTypes.includes(DETECTION_TYPES.FACE_BOX)) {
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detections = detections
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? detections.map(d => d.detection)
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: await faceapi.detectAllFaces(imageTensor, new faceapi.TinyFaceDetectorOptions());
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faceBox = await detectFaceBox({
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detections,
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threshold
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});
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}
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faceapi.tf.engine().endScope();
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if (faceBox || faceExpression) {
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self.postMessage({
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faceBox,
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faceExpression
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});
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}
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detectionInProgress = false;
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};
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const init = async ({ baseUrl, detectionTypes }) => {
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faceDetectionTypes = detectionTypes;
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if (!backendSet) {
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try {
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if (self.useWasm) {
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setWasmPaths(baseUrl);
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await faceapi.tf.setBackend('wasm');
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} else {
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await faceapi.tf.setBackend('webgl');
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}
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backendSet = true;
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} catch (err) {
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initError = true;
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return;
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}
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}
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// load face detection model
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if (!modelsLoaded) {
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try {
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await faceapi.loadTinyFaceDetectorModel(baseUrl);
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if (detectionTypes.includes(DETECTION_TYPES.FACE_EXPRESSIONS)) {
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await faceapi.loadFaceExpressionModel(baseUrl);
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}
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modelsLoaded = true;
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} catch (err) {
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initError = true;
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return;
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}
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}
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};
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onmessage = function(message) {
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switch (message.data.type) {
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case DETECT_FACE: {
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if (!backendSet || !modelsLoaded || initError || detectionInProgress) {
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return;
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}
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detect(message.data);
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break;
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}
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case INIT_WORKER: {
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init(message.data);
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break;
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}
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}
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};
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@ -0,0 +1,181 @@
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import { setWasmPaths } from '@tensorflow/tfjs-backend-wasm';
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import { Human, Config, FaceResult } from '@vladmandic/human';
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import { DETECTION_TYPES, DETECT_FACE, INIT_WORKER, FACE_EXPRESSIONS_NAMING_MAPPING } from './constants';
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type Detection = {
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detections: Array<FaceResult>,
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threshold?: number
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};
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type DetectInput = {
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image: ImageBitmap | ImageData,
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threshold: number
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};
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type FaceBox = {
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left: number,
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right: number,
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width?: number
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};
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type InitInput = {
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baseUrl: string,
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detectionTypes: string[]
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}
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/**
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* An object that is used for using human.
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*/
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let human: Human;
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/**
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* Detection types to be applied.
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*/
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let faceDetectionTypes: string[] = [];
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/**
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* Flag for indicating whether a face detection flow is in progress or not.
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*/
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let detectionInProgress = false;
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/**
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* Contains the last valid face bounding box (passes threshold validation) which was sent to the main process.
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*/
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let lastValidFaceBox: FaceBox;
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/**
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* Configuration for human.
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*/
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const config: Partial<Config> = {
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backend: 'humangl',
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async: true,
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warmup: 'none',
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cacheModels: true,
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cacheSensitivity: 0,
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debug: false,
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deallocate: true,
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filter: { enabled: false },
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face: {
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enabled: true,
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detector: {
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enabled: true,
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rotation: false
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},
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mesh: { enabled: false },
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iris: { enabled: false },
|
||||
emotion: { enabled: false },
|
||||
description: { enabled: false }
|
||||
},
|
||||
hand: { enabled: false },
|
||||
gesture: { enabled: false },
|
||||
body: { enabled: false },
|
||||
segmentation: { enabled: false }
|
||||
};
|
||||
|
||||
const detectFaceBox = async ({ detections, threshold }: Detection) => {
|
||||
if (!detections.length) {
|
||||
return null;
|
||||
}
|
||||
|
||||
const faceBox: FaceBox = {
|
||||
// normalize to percentage based
|
||||
left: Math.round(Math.min(...detections.map(d => d.boxRaw[0])) * 100),
|
||||
right: Math.round(Math.max(...detections.map(d => d.boxRaw[0] + d.boxRaw[2])) * 100)
|
||||
};
|
||||
|
||||
faceBox.width = Math.round(faceBox.right - faceBox.left);
|
||||
|
||||
if (lastValidFaceBox && threshold && Math.abs(lastValidFaceBox.left - faceBox.left) < threshold) {
|
||||
return null;
|
||||
}
|
||||
|
||||
lastValidFaceBox = faceBox;
|
||||
|
||||
return faceBox;
|
||||
};
|
||||
|
||||
const detectFaceExpression = async ({ detections }: Detection) =>
|
||||
detections[0]?.emotion && FACE_EXPRESSIONS_NAMING_MAPPING[detections[0]?.emotion[0].emotion];
|
||||
|
||||
const detect = async ({ image, threshold } : DetectInput) => {
|
||||
let detections;
|
||||
let faceExpression;
|
||||
let faceBox;
|
||||
|
||||
detectionInProgress = true;
|
||||
|
||||
const imageTensor = human.tf.browser.fromPixels(image);
|
||||
|
||||
if (faceDetectionTypes.includes(DETECTION_TYPES.FACE_EXPRESSIONS)) {
|
||||
const { face } = await human.detect(imageTensor, config);
|
||||
|
||||
detections = face;
|
||||
faceExpression = await detectFaceExpression({ detections });
|
||||
}
|
||||
|
||||
if (faceDetectionTypes.includes(DETECTION_TYPES.FACE_BOX)) {
|
||||
if (!detections) {
|
||||
const { face } = await human.detect(imageTensor, config);
|
||||
|
||||
detections = face;
|
||||
}
|
||||
|
||||
faceBox = await detectFaceBox({
|
||||
detections,
|
||||
threshold
|
||||
});
|
||||
}
|
||||
|
||||
if (faceBox || faceExpression) {
|
||||
self.postMessage({
|
||||
faceBox,
|
||||
faceExpression
|
||||
});
|
||||
}
|
||||
|
||||
detectionInProgress = false;
|
||||
};
|
||||
|
||||
const init = async ({ baseUrl, detectionTypes }: InitInput) => {
|
||||
faceDetectionTypes = detectionTypes;
|
||||
|
||||
if (!human) {
|
||||
config.modelBasePath = baseUrl;
|
||||
if (!self.OffscreenCanvas) {
|
||||
config.backend = 'wasm';
|
||||
config.wasmPath = baseUrl;
|
||||
setWasmPaths(baseUrl);
|
||||
}
|
||||
if (detectionTypes.includes(DETECTION_TYPES.FACE_EXPRESSIONS) && config.face) {
|
||||
config.face.emotion = { enabled: true };
|
||||
}
|
||||
const initialHuman = new Human(config);
|
||||
try {
|
||||
await initialHuman.load();
|
||||
} catch (err) {
|
||||
console.error(err);
|
||||
}
|
||||
|
||||
human = initialHuman;
|
||||
}
|
||||
};
|
||||
|
||||
onmessage = function(message: MessageEvent<any>) {
|
||||
switch (message.data.type) {
|
||||
case DETECT_FACE: {
|
||||
if (!human || detectionInProgress) {
|
||||
return;
|
||||
}
|
||||
|
||||
detect(message.data);
|
||||
|
||||
break;
|
||||
}
|
||||
|
||||
case INIT_WORKER: {
|
||||
init(message.data);
|
||||
break;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
|
@ -5,7 +5,7 @@ import React from 'react';
|
|||
import { Avatar, StatelessAvatar } from '../../../base/avatar';
|
||||
import { getInitials } from '../../../base/avatar/functions';
|
||||
import BaseTheme from '../../../base/ui/components/BaseTheme';
|
||||
import { FACE_EXPRESSIONS } from '../../../face-landmarks/constants.js';
|
||||
import { FACE_EXPRESSIONS } from '../../../face-landmarks/constants';
|
||||
|
||||
import TimeElapsed from './TimeElapsed';
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ import React from 'react';
|
|||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
import { Tooltip } from '../../../base/tooltip';
|
||||
import { FACE_EXPRESSIONS_EMOJIS } from '../../../face-landmarks/constants.js';
|
||||
import { FACE_EXPRESSIONS_EMOJIS } from '../../../face-landmarks/constants';
|
||||
|
||||
const useStyles = makeStyles(theme => {
|
||||
return {
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@
|
|||
"module": "es6",
|
||||
"target": "es6",
|
||||
"jsx": "react",
|
||||
"lib": [ "ES2020", "DOM" ],
|
||||
"lib": [ "webworker", "ES2020", "DOM" ],
|
||||
"noEmit": false,
|
||||
"moduleResolution": "Node",
|
||||
"strict": true,
|
||||
|
|
|
|||
|
|
@ -384,13 +384,13 @@ module.exports = (_env, argv) => {
|
|||
}),
|
||||
Object.assign({}, config, {
|
||||
entry: {
|
||||
'face-landmarks-worker': './react/features/face-landmarks/faceLandmarksWorker.js'
|
||||
'face-landmarks-worker': './react/features/face-landmarks/faceLandmarksWorker.ts'
|
||||
},
|
||||
plugins: [
|
||||
...config.plugins,
|
||||
...getBundleAnalyzerPlugin(analyzeBundle, 'face-landmarks-worker')
|
||||
],
|
||||
performance: getPerformanceHints(perfHintOptions, 1024 * 1024 * 1.5)
|
||||
performance: getPerformanceHints(perfHintOptions, 1024 * 1024 * 2)
|
||||
})
|
||||
];
|
||||
};
|
||||
|
|
|
|||
Loading…
Reference in New Issue