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human
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breaking change: convert to object class

pull/280/head
Vladimir Mandic 2020-10-19 11:03:48 -04:00
parent dfc67f0188
commit 36675c5152
6 changed files with 288 additions and 265 deletions
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74
README.md
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@ -70,23 +70,46 @@ Simply download `dist/human.js`, include it in your `HTML` file & it's ready to
<script src="dist/human.js"><script> <script src="dist/human.js"><script>
``` ```
IIFE script auto-registers global namespace `human` within global `Window` object IIFE script auto-registers global namespace `Human` within global `Window` object
Which you can use to create instance of `human` library:
```js
const human = new Human();
```
This way you can also use `Human` library within embbedded `<script>` tag within your `html` page for all-in-one approach This way you can also use `Human` library within embbedded `<script>` tag within your `html` page for all-in-one approach
### 2. [ESM](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/import) module ### 2. [ESM](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/import) module
*Recommended for usage within `Browser`* *Recommended for usage within `Browser`*
#### 2.1 With Bundler #### **2.1 Using Script Module**
You could use same syntax within your main `JS` file if it's imported with `<script type="module">`
If you're using bundler *(such as rollup, webpack, esbuild)* to package your client application, you can import ESM version of `Human` library which supports full tree shaking ```html
<script src="./index.js" type="module">
```
and then in your `index.js`
```js
import Human from 'dist/human.esm.js'; // for direct import must use path to module, not package name
const human = new Human();
```
#### **2.2 With Bundler**
If you're using bundler *(such as rollup, webpack, parcel, browserify, esbuild)* to package your client application,
you can import ESM version of `Human` library which supports full tree shaking
Install with: Install with:
```shell ```shell
npm install @vladmandic/human npm install @vladmandic/human
``` ```
```js ```js
import human from '@vladmandic/human'; // points to @vladmandic/human/dist/human.esm.js import Human from '@vladmandic/human'; // points to @vladmandic/human/dist/human.esm.js
// you can also force-load specific version
// for example: `@vladmandic/human/dist/human.esm.js`
const human = new Human();
``` ```
Or if you prefer to package your version of `tfjs`, you can use `nobundle` version Or if you prefer to package your version of `tfjs`, you can use `nobundle` version
@ -97,20 +120,8 @@ Install with:
``` ```
```js ```js
import tf from '@tensorflow/tfjs' import tf from '@tensorflow/tfjs'
import human from '@vladmandic/human/dist/human.esm-nobundle.js'; // same functionality as default import, but without tfjs bundled import Human from '@vladmandic/human/dist/human.esm-nobundle.js'; // same functionality as default import, but without tfjs bundled
``` const human = new Human();
#### 2.2 Using Script Module
You could use same syntax within your main `JS` file if it's imported with `<script type="module">`
```html
<script src="./index.js" type="module">
```
and then in your `index.js`
```js
import * as tf from `https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.6.0/tf.es2017.min.js`; // load tfjs directly from CDN link
import human from 'dist/human.esm.js'; // for direct import must use path to module, not package name
``` ```
### 3. [NPM](https://www.npmjs.com/) module ### 3. [NPM](https://www.npmjs.com/) module
@ -127,7 +138,8 @@ Install with:
And then use with: And then use with:
```js ```js
const tf = require('@tensorflow/tfjs-node'); // can also use '@tensorflow/tfjs-node-gpu' if you have environment with CUDA extensions const tf = require('@tensorflow/tfjs-node'); // can also use '@tensorflow/tfjs-node-gpu' if you have environment with CUDA extensions
const human = require('@vladmandic/human'); // points to @vladmandic/human/dist/human.cjs const Human = require('@vladmandic/human').default; // points to @vladmandic/human/dist/human.cjs
const human = new Human();
``` ```
Since NodeJS projects load `weights` from local filesystem instead of using `http` calls, you must modify default configuration to include correct paths with `file://` prefix Since NodeJS projects load `weights` from local filesystem instead of using `http` calls, you must modify default configuration to include correct paths with `file://` prefix
@ -198,13 +210,20 @@ Additionally, `Human` library exposes several objects and methods:
``` ```
Note that when using `Human` library in `NodeJS`, you must load and parse the image *before* you pass it for detection and dispose it afterwards Note that when using `Human` library in `NodeJS`, you must load and parse the image *before* you pass it for detection and dispose it afterwards
Input format is `Tensor4D[1, width, height, 3]` of type `float32`
For example: For example:
```js ```js
const imageFile = '../assets/sample1.jpg'; const imageFile = '../assets/sample1.jpg';
const buffer = fs.readFileSync(imageFile); const buffer = fs.readFileSync(imageFile);
const image = tf.node.decodeImage(buffer); const decoded = tf.node.decodeImage(buffer);
const result = human.detect(image, config); const casted = decoded.toFloat();
const image = casted.expandDims(0);
decoded.dispose();
casted.dispose();
logger.log('Processing:', image.shape);
const human = new Human.Human();
const result = await human.detect(image, config);
image.dispose(); image.dispose();
``` ```
@ -414,15 +433,15 @@ Development dependencies are [eslint](https://github.com/eslint) used for code l
Performance will vary depending on your hardware, but also on number of resolution of input video/image, enabled modules as well as their parameters Performance will vary depending on your hardware, but also on number of resolution of input video/image, enabled modules as well as their parameters
For example, on a desktop with a low-end nVidia GTX1050 it can perform multiple face detections at 60+ FPS, but drops to 10 FPS on a medium complex images if all modules are enabled For example, on a desktop with a low-end nVidia GTX1050 it can perform multiple face detections at 60+ FPS, but drops to ~15 FPS on a medium complex images if all modules are enabled
Performance per module: Performance per module:
- Enabled all: 10 FPS - Enabled all: 15 FPS
- Image filters: 80 FPS (standalone) - Image filters: 80 FPS (standalone)
- Face Detect: 80 FPS (standalone) - Face Detect: 80 FPS (standalone)
- Face Geometry: 30 FPS (includes face detect) - Face Geometry: 30 FPS (includes face detect)
- Face Iris: 25 FPS (includes face detect and face geometry) - Face Iris: 30 FPS (includes face detect and face geometry)
- Age: 60 FPS (includes face detect) - Age: 60 FPS (includes face detect)
- Gender: 60 FPS (includes face detect) - Gender: 60 FPS (includes face detect)
- Emotion: 60 FPS (includes face detect) - Emotion: 60 FPS (includes face detect)
@ -437,8 +456,11 @@ For performance details, see output of `result.performance` object during runtim
`Human` library can be used in any modern Browser or NodeJS environment, but there are several items to be aware of: `Human` library can be used in any modern Browser or NodeJS environment, but there are several items to be aware of:
- **NodeJS**: Due to a missing feature in `tfjs-node`, only some models are available <https://github.com/tensorflow/tfjs/issues/4066> - **NodeJS**: Due to a missing feature in `tfjs-node`, only some models are available
- **Browser**: `filters` module cannot be used when using web workers <https://github.com/phoboslab/WebGLImageFilter/issues/27> For unsupported models, error is: `TypeError: forwardFunc is not a function`
<https://github.com/tensorflow/tfjs/issues/4066>
- **Browser**: Module `filters` cannot be used when using web workers
<https://github.com/phoboslab/WebGLImageFilter/issues/27>
<hr> <hr>

4
demo/browser.js
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@ -1,7 +1,9 @@
import human from '../dist/human.esm.js'; import Human from '../dist/human.esm.js';
import draw from './draw.js'; import draw from './draw.js';
import Menu from './menu.js'; import Menu from './menu.js';
const human = new Human();
// ui options // ui options
const ui = { const ui = {
baseColor: 'rgba(173, 216, 230, 0.3)', // this is 'lightblue', just with alpha channel baseColor: 'rgba(173, 216, 230, 0.3)', // this is 'lightblue', just with alpha channel

10
demo/node.js
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@ -2,7 +2,7 @@ const tf = require('@tensorflow/tfjs-node');
const fs = require('fs'); const fs = require('fs');
const process = require('process'); const process = require('process');
const console = require('console'); const console = require('console');
const human = require('..'); // this resolves to project root which is '@vladmandic/human' const Human = require('..').default; // this resolves to project root which is '@vladmandic/human'
const logger = new console.Console({ const logger = new console.Console({
stdout: process.stdout, stdout: process.stdout,
@ -26,6 +26,7 @@ const logger = new console.Console({
const config = { const config = {
backend: 'tensorflow', backend: 'tensorflow',
console: true, console: true,
videoOptimized: false,
face: { face: {
detector: { modelPath: 'file://models/blazeface/back/model.json' }, detector: { modelPath: 'file://models/blazeface/back/model.json' },
mesh: { modelPath: 'file://models/facemesh/model.json' }, mesh: { modelPath: 'file://models/facemesh/model.json' },
@ -47,8 +48,13 @@ async function detect(input, output) {
logger.info('TFJS Flags:', tf.env().features); logger.info('TFJS Flags:', tf.env().features);
logger.log('Loading:', input); logger.log('Loading:', input);
const buffer = fs.readFileSync(input); const buffer = fs.readFileSync(input);
const image = tf.node.decodeImage(buffer); const decoded = tf.node.decodeImage(buffer);
const casted = decoded.toFloat();
const image = casted.expandDims(0);
decoded.dispose();
casted.dispose();
logger.log('Processing:', image.shape); logger.log('Processing:', image.shape);
const human = new Human();
const result = await human.detect(image, config); const result = await human.detect(image, config);
image.dispose(); image.dispose();
logger.log(result); logger.log(result);

3
demo/worker.js
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@ -1,7 +1,8 @@
import human from '../dist/human.esm.js'; import Human from '../dist/human.esm.js';
let config; let config;
let busy = false; let busy = false;
const human = new Human();
const log = (...msg) => { const log = (...msg) => {
// eslint-disable-next-line no-console // eslint-disable-next-line no-console

2
package.json
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@ -39,7 +39,7 @@
"scripts": { "scripts": {
"start": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js", "start": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",
"lint": "eslint src/*.js demo/*.js", "lint": "eslint src/*.js demo/*.js",
"build-iife": "esbuild --bundle --platform=browser --sourcemap --target=esnext --format=iife --minify --external:fs --global-name=human --metafile=dist/human.json --outfile=dist/human.js src/human.js", "build-iife": "esbuild --bundle --platform=browser --sourcemap --target=esnext --format=iife --minify --external:fs --global-name=Human --metafile=dist/human.json --outfile=dist/human.js src/human.js",
"build-esm-bundle": "esbuild --bundle --platform=browser --sourcemap --target=esnext --format=esm --minify --external:fs --metafile=dist/human.esm.json --outfile=dist/human.esm.js src/human.js", "build-esm-bundle": "esbuild --bundle --platform=browser --sourcemap --target=esnext --format=esm --minify --external:fs --metafile=dist/human.esm.json --outfile=dist/human.esm.js src/human.js",
"build-esm-nobundle": "esbuild --bundle --platform=browser --sourcemap --target=esnext --format=esm --minify --external:@tensorflow --external:fs --metafile=dist/human.esm-nobundle.json --outfile=dist/human.esm-nobundle.js src/human.js", "build-esm-nobundle": "esbuild --bundle --platform=browser --sourcemap --target=esnext --format=esm --minify --external:@tensorflow --external:fs --metafile=dist/human.esm-nobundle.json --outfile=dist/human.esm-nobundle.js src/human.js",
"build-node": "esbuild --bundle --platform=node --sourcemap --target=esnext --format=cjs --external:@tensorflow --metafile=dist/human.cjs.json --outfile=dist/human.cjs src/human.js", "build-node": "esbuild --bundle --platform=node --sourcemap --target=esnext --format=cjs --external:@tensorflow --metafile=dist/human.cjs.json --outfile=dist/human.cjs src/human.js",

276
src/human.js
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@ -8,22 +8,7 @@ const fxImage = require('./imagefx.js');
const defaults = require('../config.js').default; const defaults = require('../config.js').default;
const app = require('../package.json'); const app = require('../package.json');
let config; // static config override for non-video detection
let fx;
let state = 'idle';
let offscreenCanvas;
// object that contains all initialized models
const models = {
facemesh: null,
posenet: null,
handpose: null,
iris: null,
age: null,
gender: null,
emotion: null,
};
const override = { const override = {
face: { detector: { skipFrames: 0 }, age: { skipFrames: 0 }, emotion: { skipFrames: 0 } }, face: { detector: { skipFrames: 0 }, age: { skipFrames: 0 }, emotion: { skipFrames: 0 } },
hand: { skipFrames: 0 }, hand: { skipFrames: 0 },
@ -35,24 +20,6 @@ const now = () => {
return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000); return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);
}; };
// helper function: wrapper around console output
const log = (...msg) => {
// eslint-disable-next-line no-console
if (msg && config.console) console.log(...msg);
};
// helper function: measure tensor leak
let numTensors = 0;
const analyzeMemoryLeaks = false;
const analyze = (...msg) => {
if (!analyzeMemoryLeaks) return;
const current = tf.engine().state.numTensors;
const previous = numTensors;
numTensors = current;
const leaked = current - previous;
if (leaked !== 0) log(...msg, leaked);
};
// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides // helper function: perform deep merge of multiple objects so it allows full inheriance with overrides
function mergeDeep(...objects) { function mergeDeep(...objects) {
const isObject = (obj) => obj && typeof obj === 'object'; const isObject = (obj) => obj && typeof obj === 'object';
@ -85,68 +52,104 @@ function sanity(input) {
return null; return null;
} }
async function load(userConfig) { class Human {
if (userConfig) config = mergeDeep(defaults, userConfig); constructor() {
if (config.face.enabled && !models.facemesh) { this.tf = tf;
log('Load model: Face'); this.version = app.version;
models.facemesh = await facemesh.load(config.face); this.defaults = defaults;
this.config = defaults;
this.fx = (tf.ENV.flags.IS_BROWSER && (typeof document !== 'undefined')) ? new fxImage.Canvas() : null;
this.state = 'idle';
this.numTensors = 0;
this.analyzeMemoryLeaks = false;
// object that contains all initialized models
this.models = {
facemesh: null,
posenet: null,
handpose: null,
iris: null,
age: null,
gender: null,
emotion: null,
};
// export raw access to underlying models
this.facemesh = facemesh;
this.ssrnet = ssrnet;
this.emotion = emotion;
this.posenet = posenet;
this.handpose = handpose;
} }
if (config.body.enabled && !models.posenet) {
log('Load model: Body');
models.posenet = await posenet.load(config.body);
}
if (config.hand.enabled && !models.handpose) {
log('Load model: Hand');
models.handpose = await handpose.load(config.hand);
}
if (config.face.enabled && config.face.age.enabled && !models.age) {
log('Load model: Age');
models.age = await ssrnet.loadAge(config);
}
if (config.face.enabled && config.face.gender.enabled && !models.gender) {
log('Load model: Gender');
models.gender = await ssrnet.loadGender(config);
}
if (config.face.enabled && config.face.emotion.enabled && !models.emotion) {
log('Load model: Emotion');
models.emotion = await emotion.load(config);
}
}
function tfImage(input) { // helper function: wrapper around console output
log(...msg) {
// eslint-disable-next-line no-console
if (msg && this.config.console) console.log(...msg);
}
// helper function: measure tensor leak
analyze(...msg) {
if (!this.analyzeMemoryLeaks) return;
const current = tf.engine().state.numTensors;
const previous = this.numTensors;
this.numTensors = current;
const leaked = current - previous;
if (leaked !== 0) this.log(...msg, leaked);
}
async load(userConfig) {
if (userConfig) this.config = mergeDeep(defaults, userConfig);
if (this.config.face.enabled && !this.models.facemesh) {
this.log('Load model: Face');
this.models.facemesh = await facemesh.load(this.config.face);
}
if (this.config.body.enabled && !this.models.posenet) {
this.log('Load model: Body');
this.models.posenet = await posenet.load(this.config.body);
}
if (this.config.hand.enabled && !this.models.handpose) {
this.log('Load model: Hand');
this.models.handpose = await handpose.load(this.config.hand);
}
if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) {
this.log('Load model: Age');
this.models.age = await ssrnet.loadAge(this.config);
}
if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) {
this.log('Load model: Gender');
this.models.gender = await ssrnet.loadGender(this.config);
}
if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) {
this.log('Load model: Emotion');
this.models.emotion = await emotion.load(this.config);
}
}
tfImage(input) {
// let imageData; // let imageData;
let filtered; let filtered;
if (tf.ENV.flags.IS_BROWSER && config.filter.enabled && !(input instanceof tf.Tensor)) { if (this.fx && this.config.filter.enabled && !(input instanceof tf.Tensor)) {
const width = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0)); const width = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));
const height = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0)); const height = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));
if (!offscreenCanvas) offscreenCanvas = new OffscreenCanvas(width, height); const offscreenCanvas = new OffscreenCanvas(width, height);
/*
if (!offscreenCanvas) {
offscreenCanvas = document.createElement('canvas');
offscreenCanvas.width = width;
offscreenCanvas.height = height;
}
*/
const ctx = offscreenCanvas.getContext('2d'); const ctx = offscreenCanvas.getContext('2d');
if (input instanceof ImageData) ctx.putImageData(input, 0, 0); if (input instanceof ImageData) ctx.putImageData(input, 0, 0);
else ctx.drawImage(input, 0, 0, width, height, 0, 0, offscreenCanvas.width, offscreenCanvas.height); else ctx.drawImage(input, 0, 0, width, height, 0, 0, offscreenCanvas.width, offscreenCanvas.height);
if (!fx) fx = new fxImage.Canvas(); this.fx.reset();
else fx.reset(); this.fx.addFilter('brightness', this.config.filter.brightness); // must have at least one filter enabled
fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled if (this.config.filter.contrast !== 0) this.fx.addFilter('contrast', this.config.filter.contrast);
if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast); if (this.config.filter.sharpness !== 0) this.fx.addFilter('sharpen', this.config.filter.sharpness);
if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness); if (this.config.filter.blur !== 0) this.fx.addFilter('blur', this.config.filter.blur);
if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur); if (this.config.filter.saturation !== 0) this.fx.addFilter('saturation', this.config.filter.saturation);
if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation); if (this.config.filter.hue !== 0) this.fx.addFilter('hue', this.config.filter.hue);
if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue); if (this.config.filter.negative) this.fx.addFilter('negative');
if (config.filter.negative) fx.addFilter('negative'); if (this.config.filter.sepia) this.fx.addFilter('sepia');
if (config.filter.sepia) fx.addFilter('sepia'); if (this.config.filter.vintage) this.fx.addFilter('brownie');
if (config.filter.vintage) fx.addFilter('brownie'); if (this.config.filter.sepia) this.fx.addFilter('sepia');
if (config.filter.sepia) fx.addFilter('sepia'); if (this.config.filter.kodachrome) this.fx.addFilter('kodachrome');
if (config.filter.kodachrome) fx.addFilter('kodachrome'); if (this.config.filter.technicolor) this.fx.addFilter('technicolor');
if (config.filter.technicolor) fx.addFilter('technicolor'); if (this.config.filter.polaroid) this.fx.addFilter('polaroid');
if (config.filter.polaroid) fx.addFilter('polaroid'); if (this.config.filter.pixelate !== 0) this.fx.addFilter('pixelate', this.config.filter.pixelate);
if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate); filtered = this.fx.apply(offscreenCanvas);
filtered = fx.apply(offscreenCanvas);
} }
let tensor; let tensor;
if (input instanceof tf.Tensor) { if (input instanceof tf.Tensor) {
@ -158,25 +161,25 @@ function tfImage(input) {
pixels.dispose(); pixels.dispose();
casted.dispose(); casted.dispose();
} }
return { tensor, canvas: config.filter.return ? filtered : null }; return { tensor, canvas: this.config.filter.return ? filtered : null };
} }
async function detect(input, userConfig = {}) { async detect(input, userConfig = {}) {
state = 'config'; this.state = 'config';
const perf = {}; const perf = {};
let timeStamp; let timeStamp;
timeStamp = now(); timeStamp = now();
config = mergeDeep(defaults, userConfig); this.config = mergeDeep(defaults, userConfig);
if (!config.videoOptimized) config = mergeDeep(config, override); if (!this.config.videoOptimized) this.config = mergeDeep(this.config, override);
perf.config = Math.trunc(now() - timeStamp); perf.config = Math.trunc(now() - timeStamp);
// sanity checks // sanity checks
timeStamp = now(); timeStamp = now();
state = 'check'; this.state = 'check';
const error = sanity(input); const error = sanity(input);
if (error) { if (error) {
log(error, input); this.log(error, input);
return { error }; return { error };
} }
perf.sanity = Math.trunc(now() - timeStamp); perf.sanity = Math.trunc(now() - timeStamp);
@ -187,76 +190,76 @@ async function detect(input, userConfig = {}) {
// configure backend // configure backend
timeStamp = now(); timeStamp = now();
if (tf.getBackend() !== config.backend) { if (tf.getBackend() !== this.config.backend) {
state = 'backend'; this.state = 'backend';
log('Human library setting backend:', config.backend); this.log('Human library setting backend:', this.config.backend);
await tf.setBackend(config.backend); await tf.setBackend(this.config.backend);
await tf.ready(); await tf.ready();
} }
perf.backend = Math.trunc(now() - timeStamp); perf.backend = Math.trunc(now() - timeStamp);
// check number of loaded models // check number of loaded models
const loadedModels = Object.values(models).filter((a) => a).length; const loadedModels = Object.values(this.models).filter((a) => a).length;
if (loadedModels === 0) { if (loadedModels === 0) {
log('Human library starting'); this.log('Human library starting');
log('Configuration:', config); this.log('Configuration:', this.config);
log('Flags:', tf.ENV.flags); this.log('Flags:', tf.ENV.flags);
} }
// load models if enabled // load models if enabled
timeStamp = now(); timeStamp = now();
state = 'load'; this.state = 'load';
await load(); await this.load();
perf.load = Math.trunc(now() - timeStamp); perf.load = Math.trunc(now() - timeStamp);
if (config.scoped) tf.engine().startScope(); if (this.config.scoped) tf.engine().startScope();
analyze('Start Detect:'); this.analyze('Start Detect:');
timeStamp = now(); timeStamp = now();
const image = tfImage(input); const image = this.tfImage(input);
perf.image = Math.trunc(now() - timeStamp); perf.image = Math.trunc(now() - timeStamp);
const imageTensor = image.tensor; const imageTensor = image.tensor;
// run posenet // run posenet
state = 'run:body'; this.state = 'run:body';
timeStamp = now(); timeStamp = now();
analyze('Start PoseNet'); this.analyze('Start PoseNet');
const poseRes = config.body.enabled ? await models.posenet.estimatePoses(imageTensor, config.body) : []; const poseRes = this.config.body.enabled ? await this.models.posenet.estimatePoses(imageTensor, this.config.body) : [];
analyze('End PoseNet:'); this.analyze('End PoseNet:');
perf.body = Math.trunc(now() - timeStamp); perf.body = Math.trunc(now() - timeStamp);
// run handpose // run handpose
state = 'run:hand'; this.state = 'run:hand';
timeStamp = now(); timeStamp = now();
analyze('Start HandPose:'); this.analyze('Start HandPose:');
const handRes = config.hand.enabled ? await models.handpose.estimateHands(imageTensor, config.hand) : []; const handRes = this.config.hand.enabled ? await this.models.handpose.estimateHands(imageTensor, this.config.hand) : [];
analyze('End HandPose:'); this.analyze('End HandPose:');
perf.hand = Math.trunc(now() - timeStamp); perf.hand = Math.trunc(now() - timeStamp);
// run facemesh, includes blazeface and iris // run facemesh, includes blazeface and iris
const faceRes = []; const faceRes = [];
if (config.face.enabled) { if (this.config.face.enabled) {
state = 'run:face'; this.state = 'run:face';
timeStamp = now(); timeStamp = now();
analyze('Start FaceMesh:'); this.analyze('Start FaceMesh:');
const faces = await models.facemesh.estimateFaces(imageTensor, config.face); const faces = await this.models.facemesh.estimateFaces(imageTensor, this.config.face);
perf.face = Math.trunc(now() - timeStamp); perf.face = Math.trunc(now() - timeStamp);
for (const face of faces) { for (const face of faces) {
// is something went wrong, skip the face // is something went wrong, skip the face
if (!face.image || face.image.isDisposedInternal) { if (!face.image || face.image.isDisposedInternal) {
log('face object is disposed:', face.image); this.log('face object is disposed:', face.image);
continue; continue;
} }
// run ssr-net age & gender, inherits face from blazeface // run ssr-net age & gender, inherits face from blazeface
state = 'run:agegender'; this.state = 'run:agegender';
timeStamp = now(); timeStamp = now();
const ssrData = (config.face.age.enabled || config.face.gender.enabled) ? await ssrnet.predict(face.image, config) : {}; const ssrData = (this.config.face.age.enabled || this.config.face.gender.enabled) ? await ssrnet.predict(face.image, this.config) : {};
perf.agegender = Math.trunc(now() - timeStamp); perf.agegender = Math.trunc(now() - timeStamp);
// run emotion, inherits face from blazeface // run emotion, inherits face from blazeface
state = 'run:emotion'; this.state = 'run:emotion';
timeStamp = now(); timeStamp = now();
const emotionData = config.face.emotion.enabled ? await emotion.predict(face.image, config) : {}; const emotionData = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};
perf.emotion = Math.trunc(now() - timeStamp); perf.emotion = Math.trunc(now() - timeStamp);
// dont need face anymore // dont need face anymore
@ -277,31 +280,20 @@ async function detect(input, userConfig = {}) {
emotion: emotionData, emotion: emotionData,
iris: (iris !== 0) ? Math.trunc(100 * 11.7 /* human iris size in mm */ / iris) / 100 : 0, iris: (iris !== 0) ? Math.trunc(100 * 11.7 /* human iris size in mm */ / iris) / 100 : 0,
}); });
analyze('End FaceMesh:'); this.analyze('End FaceMesh:');
} }
} }
imageTensor.dispose(); imageTensor.dispose();
state = 'idle'; this.state = 'idle';
if (config.scoped) tf.engine().endScope(); if (this.config.scoped) tf.engine().endScope();
analyze('End Scope:'); this.analyze('End Scope:');
perf.total = Math.trunc(now() - timeStart); perf.total = Math.trunc(now() - timeStart);
resolve({ face: faceRes, body: poseRes, hand: handRes, performance: perf, canvas: image.canvas }); resolve({ face: faceRes, body: poseRes, hand: handRes, performance: perf, canvas: image.canvas });
}); });
}
} }
exports.detect = detect; export { Human as default };
exports.defaults = defaults;
exports.config = config;
exports.models = models;
exports.facemesh = facemesh;
exports.ssrnet = ssrnet;
exports.posenet = posenet;
exports.handpose = handpose;
exports.tf = tf;
exports.version = app.version;
exports.state = state;
// Error: Failed to compile fragment shader