mirror of https://github.com/vladmandic/human
added state handling
Vladimir Mandic
parent
9f86c24fe3
commit
f3bf35533e
55
README.md
55
README.md
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@ -133,15 +133,6 @@ const config = {
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}
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```
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Note that when using `Human` in NodeJS, you must load and parse the image *before* you pass it for detection
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For example:
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```js
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const buffer = fs.readFileSync(input);
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const image = tf.node.decodeImage(buffer);
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const result = human.detect(image, config);
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image.dispose();
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```
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### Weights
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Pretrained model weights are includes in `./models`
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@ -167,34 +158,48 @@ NodeJS:
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## Usage
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`Human` library does not require special initialization.
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All configuration is done in a single JSON object and all model weights will be dynamically loaded upon their first usage(and only then, `Human` will not load weights that it doesn't need according to configuration).
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All configuration is done in a single JSON object and all model weights will be dynamically loaded upon their first usage
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(and only then, `Human` will not load weights that it doesn't need according to configuration).
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There is only *ONE* method you need:
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```js
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import * as tf from '@tensorflow/tfjs';
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import human from '@vladmandic/human';
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// 'image': can be of any type of an image object: HTMLImage, HTMLVideo, HTMLMedia, Canvas, Tensor4D
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// 'options': optional parameter used to override any options present in default configuration
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const result = await human.detect(image, options?)
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// 'image': can be of any type of an image object: HTMLImage, HTMLVideo, HTMLMedia, Canvas, Tensor4D
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// 'config': optional parameter used to override any options present in default configuration
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// configuration is fully dynamic and can change between different calls to 'detect()'
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const result = await human.detect(image, config?)
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```
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or if you want to use promises
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```js
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human.detect(image, options?).then((result) => {
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human.detect(image, config?).then((result) => {
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// your code
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})
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```
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Additionally, `Human` library exposes several classes:
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Additionally, `Human` library exposes several objects and methods:
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```js
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human.config // access to configuration object, normally set as parameter to detect()
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human.defaults // read-only view of default configuration object
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human.models // dynamically maintained list of object of any loaded models
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human.tf // instance of tfjs used by human
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human.config // access to configuration object, normally set as parameter to detect()
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human.defaults // read-only view of default configuration object
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human.models // dynamically maintained list of object of any loaded models
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human.tf // instance of tfjs used by human
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human.state // <string> describing current operation in progress
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// progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
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human.load(config) // explicitly call load method that loads configured models
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// if you want to pre-load them instead of on-demand loading during 'human.detect()'
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```
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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
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For example:
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```js
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const imageFile = '../assets/sample1.jpg';
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const buffer = fs.readFileSync(imageFile);
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const image = tf.node.decodeImage(buffer);
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const result = human.detect(image, config);
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image.dispose();
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```
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<hr>
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@ -213,7 +218,7 @@ Configurtion object is large, but typically you only need to modify few values:
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```js
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export default {
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config = {
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backend: 'webgl', // select tfjs backend to use
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console: true, // enable debugging output to console
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face: {
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@ -221,9 +226,9 @@ export default {
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// face.enabled is required for all face models: detector, mesh, iris, age, gender, emotion
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// note: module is not loaded until it is required
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detector: {
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modelPath: '../models/blazeface/back/model.json', // can be 'tfhub', 'front' or 'back'.
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modelPath: '../models/blazeface/back/model.json', // can be 'front' or 'back'.
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// 'front' is optimized for large faces such as front-facing camera and 'back' is optimized for distanct faces.
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inputSize: 256, // fixed value: 128 for front and 'tfhub' and 'front' and 256 for 'back'
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inputSize: 256, // fixed value: 128 for front and 256 for 'back'
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maxFaces: 10, // maximum number of faces detected in the input, should be set to the minimum number for performance
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skipFrames: 10, // how many frames to go without re-running the face bounding box detector
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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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@ -9,9 +9,9 @@ export default {
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// face.enabled is required for all face models: detector, mesh, iris, age, gender, emotion
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// (note: module is not loaded until it is required)
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detector: {
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modelPath: '../models/blazeface/back/model.json', // can be 'tfhub', 'front' or 'back'.
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modelPath: '../models/blazeface/back/model.json', // can be 'front' or 'back'.
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// 'front' is optimized for large faces such as front-facing camera and 'back' is optimized for distanct faces.
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inputSize: 256, // fixed value: 128 for front and 'tfhub' and 'front' and 256 for 'back'
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inputSize: 256, // fixed value: 128 for front and 256 for 'back'
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maxFaces: 10, // maximum number of faces detected in the input, should be set to the minimum number for performance
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skipFrames: 10, // how many frames to go without re-running the face bounding box detector
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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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179
demo/browser.js
179
demo/browser.js
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@ -5,13 +5,14 @@ import human from '../dist/human.esm.js';
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const ui = {
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baseColor: 'rgba(255, 200, 255, 0.3)',
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baseLabel: 'rgba(255, 200, 255, 0.9)',
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baseFont: 'small-caps 1.2rem "Segoe UI"',
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baseFontProto: 'small-caps {size} "Segoe UI"',
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baseLineWidth: 16,
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baseLineHeight: 2,
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baseLineHeightProto: 2,
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columns: 3,
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busy: false,
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facing: 'user',
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worker: 'worker.js',
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samples: ['../assets/sample1.jpg', '../assets/sample2.jpg', '../assets/sample3.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample6.jpg'],
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};
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const config = {
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@ -285,82 +286,6 @@ async function runHumanDetect(input, canvas) {
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}
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}
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function setupUI() {
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// add all variables to ui control panel
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settings = QuickSettings.create(10, 10, 'Settings', document.getElementById('main'));
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const style = document.createElement('style');
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// style.type = 'text/css';
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style.innerHTML = `
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.qs_main { font: 1rem "Segoe UI"; }
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.qs_label { font: 0.8rem "Segoe UI"; }
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.qs_content { background: darkslategray; }
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.qs_container { background: transparent; color: white; margin: 6px; padding: 6px; }
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.qs_checkbox_label { top: 2px; }
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.qs_button { width: -webkit-fill-available; font: 1rem "Segoe UI"; cursor: pointer; }
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`;
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document.getElementsByTagName('head')[0].appendChild(style);
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settings.addButton('Play/Pause', () => {
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const video = document.getElementById('video');
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const canvas = document.getElementById('canvas');
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if (!video.paused) {
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document.getElementById('log').innerText += '\nPaused ...';
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video.pause();
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} else {
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document.getElementById('log').innerText += '\nStarting Human Library ...';
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video.play();
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}
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runHumanDetect(video, canvas);
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});
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settings.addDropDown('Backend', ['webgl', 'wasm', 'cpu'], async (val) => config.backend = val.value);
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settings.addHTML('title', 'Enabled Models'); settings.hideTitle('title');
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settings.addBoolean('Face Detect', config.face.enabled, (val) => config.face.enabled = val);
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settings.addBoolean('Face Mesh', config.face.mesh.enabled, (val) => config.face.mesh.enabled = val);
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settings.addBoolean('Face Iris', config.face.iris.enabled, (val) => config.face.iris.enabled = val);
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settings.addBoolean('Face Age', config.face.age.enabled, (val) => config.face.age.enabled = val);
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settings.addBoolean('Face Gender', config.face.gender.enabled, (val) => config.face.gender.enabled = val);
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settings.addBoolean('Face Emotion', config.face.emotion.enabled, (val) => config.face.emotion.enabled = val);
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settings.addBoolean('Body Pose', config.body.enabled, (val) => config.body.enabled = val);
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settings.addBoolean('Hand Pose', config.hand.enabled, (val) => config.hand.enabled = val);
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settings.addHTML('title', 'Model Parameters'); settings.hideTitle('title');
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settings.addRange('Max Objects', 1, 20, 5, 1, (val) => {
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config.face.detector.maxFaces = parseInt(val);
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config.body.maxDetections = parseInt(val);
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});
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settings.addRange('Skip Frames', 1, 20, config.face.detector.skipFrames, 1, (val) => {
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config.face.detector.skipFrames = parseInt(val);
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config.face.emotion.skipFrames = parseInt(val);
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config.face.age.skipFrames = parseInt(val);
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config.hand.skipFrames = parseInt(val);
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});
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settings.addRange('Min Confidence', 0.1, 1.0, config.face.detector.minConfidence, 0.05, (val) => {
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config.face.detector.minConfidence = parseFloat(val);
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config.face.emotion.minConfidence = parseFloat(val);
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config.hand.minConfidence = parseFloat(val);
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});
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settings.addRange('Score Threshold', 0.1, 1.0, config.face.detector.scoreThreshold, 0.05, (val) => {
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config.face.detector.scoreThreshold = parseFloat(val);
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config.hand.scoreThreshold = parseFloat(val);
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config.body.scoreThreshold = parseFloat(val);
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});
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settings.addRange('IOU Threshold', 0.1, 1.0, config.face.detector.iouThreshold, 0.05, (val) => {
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config.face.detector.iouThreshold = parseFloat(val);
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config.hand.iouThreshold = parseFloat(val);
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});
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settings.addHTML('title', 'UI Options'); settings.hideTitle('title');
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settings.addBoolean('Use Web Worker', false);
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settings.addBoolean('Camera Front/Back', true, (val) => {
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ui.facing = val ? 'user' : 'environment';
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// eslint-disable-next-line no-use-before-define
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setupCamera();
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});
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settings.addBoolean('Draw Boxes', true);
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settings.addBoolean('Draw Points', true);
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settings.addBoolean('Draw Polygons', true);
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settings.addBoolean('Fill Polygons', true);
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settings.addHTML('line1', '<hr>'); settings.hideTitle('line1');
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settings.addRange('FPS', 0, 100, 0, 1);
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}
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// eslint-disable-next-line no-unused-vars
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async function setupCamera() {
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if (ui.busy) return null;
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@ -409,7 +334,6 @@ async function processImage(input) {
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ui.baseLabel = 'rgba(200, 255, 255, 0.8)';
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ui.baseFont = 'small-caps 3.5rem "Segoe UI"';
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ui.baseLineWidth = 16;
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ui.baseLineHeight = 5;
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ui.columns = 3;
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const cfg = {
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backend: 'webgl',
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@ -450,28 +374,105 @@ async function processImage(input) {
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});
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}
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async function detectVideo() {
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document.getElementById('samples').style.display = 'none';
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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}/, '1.2rem');
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ui.baseLineHeight = ui.baseLineHeightProto;
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if (!video.paused) {
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document.getElementById('log').innerText += '\nPaused ...';
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video.pause();
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} else {
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await setupCamera();
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document.getElementById('log').innerText += '\nStarting Human Library ...';
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video.play();
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}
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runHumanDetect(video, canvas);
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}
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// eslint-disable-next-line no-unused-vars
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async function detectSampleImages() {
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ui.baseFont = 'small-caps 3rem "Segoe UI"';
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ui.baseFont = ui.baseFontProto.replace(/{size}/, `${ui.columns}rem`);
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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').style.display = 'block';
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log('Running detection of sample images');
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const samples = ['../assets/sample1.jpg', '../assets/sample2.jpg', '../assets/sample3.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample6.jpg'];
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for (const sample of samples) await processImage(sample);
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for (const sample of ui.samples) await processImage(sample);
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}
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function setupUI() {
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// add all variables to ui control panel
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settings = QuickSettings.create(10, 10, 'Settings', document.getElementById('main'));
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const style = document.createElement('style');
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style.innerHTML = `
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.qs_main { font: 1rem "Segoe UI"; }
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.qs_label { font: 0.8rem "Segoe UI"; }
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.qs_content { background: darkslategray; }
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.qs_container { background: transparent; color: white; margin: 6px; padding: 6px; }
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.qs_checkbox_label { top: 2px; }
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.qs_button { width: -webkit-fill-available; font: 1rem "Segoe UI"; cursor: pointer; }
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`;
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document.getElementsByTagName('head')[0].appendChild(style);
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settings.addButton('Play/Pause WebCam', () => detectVideo());
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settings.addButton('Process Images', () => detectSampleImages());
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settings.addDropDown('Backend', ['webgl', 'wasm', 'cpu'], async (val) => config.backend = val.value);
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settings.addHTML('title', 'Enabled Models'); settings.hideTitle('title');
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settings.addBoolean('Face Detect', config.face.enabled, (val) => config.face.enabled = val);
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settings.addBoolean('Face Mesh', config.face.mesh.enabled, (val) => config.face.mesh.enabled = val);
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settings.addBoolean('Face Iris', config.face.iris.enabled, (val) => config.face.iris.enabled = val);
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settings.addBoolean('Face Age', config.face.age.enabled, (val) => config.face.age.enabled = val);
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settings.addBoolean('Face Gender', config.face.gender.enabled, (val) => config.face.gender.enabled = val);
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settings.addBoolean('Face Emotion', config.face.emotion.enabled, (val) => config.face.emotion.enabled = val);
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settings.addBoolean('Body Pose', config.body.enabled, (val) => config.body.enabled = val);
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settings.addBoolean('Hand Pose', config.hand.enabled, (val) => config.hand.enabled = val);
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settings.addHTML('title', 'Model Parameters'); settings.hideTitle('title');
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settings.addRange('Max Objects', 1, 20, 5, 1, (val) => {
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config.face.detector.maxFaces = parseInt(val);
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config.body.maxDetections = parseInt(val);
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});
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settings.addRange('Skip Frames', 1, 20, config.face.detector.skipFrames, 1, (val) => {
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config.face.detector.skipFrames = parseInt(val);
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config.face.emotion.skipFrames = parseInt(val);
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config.face.age.skipFrames = parseInt(val);
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config.hand.skipFrames = parseInt(val);
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});
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settings.addRange('Min Confidence', 0.1, 1.0, config.face.detector.minConfidence, 0.05, (val) => {
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config.face.detector.minConfidence = parseFloat(val);
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config.face.emotion.minConfidence = parseFloat(val);
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config.hand.minConfidence = parseFloat(val);
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});
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settings.addRange('Score Threshold', 0.1, 1.0, config.face.detector.scoreThreshold, 0.05, (val) => {
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config.face.detector.scoreThreshold = parseFloat(val);
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config.hand.scoreThreshold = parseFloat(val);
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config.body.scoreThreshold = parseFloat(val);
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});
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settings.addRange('IOU Threshold', 0.1, 1.0, config.face.detector.iouThreshold, 0.05, (val) => {
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config.face.detector.iouThreshold = parseFloat(val);
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config.hand.iouThreshold = parseFloat(val);
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});
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settings.addHTML('title', 'UI Options'); settings.hideTitle('title');
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settings.addBoolean('Use Web Worker', false);
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settings.addBoolean('Camera Front/Back', true, (val) => {
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ui.facing = val ? 'user' : 'environment';
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setupCamera();
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});
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settings.addBoolean('Draw Boxes', true);
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settings.addBoolean('Draw Points', true);
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settings.addBoolean('Draw Polygons', true);
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settings.addBoolean('Fill Polygons', true);
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settings.addHTML('line1', '<hr>'); settings.hideTitle('line1');
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settings.addRange('FPS', 0, 100, 0, 1);
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}
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async function main() {
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log('Human demo starting ...');
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// setup ui control panel
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await setupUI();
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setupUI();
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const msg = `Human ready: version: ${human.version} TensorFlow/JS version: ${human.tf.version_core}`;
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document.getElementById('log').innerText += '\n' + msg;
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log(msg);
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// use one of the two:
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await setupCamera();
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// await detectSampleImages();
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}
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window.onload = main;
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43
src/index.js
43
src/index.js
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@ -8,6 +8,7 @@ const defaults = require('../config.js').default;
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const app = require('../package.json');
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let config;
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let state = 'idle';
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// object that contains all initialized models
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const models = {
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@ -61,10 +62,22 @@ function sanity(input) {
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return null;
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}
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async function detect(input, userConfig) {
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async function load(userConfig) {
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if (userConfig) config = mergeDeep(defaults, userConfig);
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if (config.face.enabled && !models.facemesh) models.facemesh = await facemesh.load(config.face);
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if (config.body.enabled && !models.posenet) models.posenet = await posenet.load(config.body);
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if (config.hand.enabled && !models.handpose) models.handpose = await handpose.load(config.hand);
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if (config.face.enabled && config.face.age.enabled && !models.age) models.age = await ssrnet.loadAge(config);
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if (config.face.enabled && config.face.gender.enabled && !models.gender) models.gender = await ssrnet.loadGender(config);
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if (config.face.enabled && config.face.emotion.enabled && !models.emotion) models.emotion = await emotion.load(config);
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}
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async function detect(input, userConfig = {}) {
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state = 'config';
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config = mergeDeep(defaults, userConfig);
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// sanity checks
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state = 'check';
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const error = sanity(input);
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if (error) {
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log(error, input);
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@ -79,6 +92,7 @@ async function detect(input, userConfig) {
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// configure backend
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if (tf.getBackend() !== config.backend) {
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state = 'backend';
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log('Human library setting backend:', config.backend);
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await tf.setBackend(config.backend);
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await tf.ready();
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@ -91,35 +105,31 @@ async function detect(input, userConfig) {
|
|||
// }
|
||||
|
||||
// load models if enabled
|
||||
if (config.face.enabled && !models.facemesh) models.facemesh = await facemesh.load(config.face);
|
||||
if (config.body.enabled && !models.posenet) models.posenet = await posenet.load(config.body);
|
||||
if (config.hand.enabled && !models.handpose) models.handpose = await handpose.load(config.hand);
|
||||
if (config.face.enabled && config.face.age.enabled && !models.age) models.age = await ssrnet.loadAge(config);
|
||||
if (config.face.enabled && config.face.gender.enabled && !models.gender) models.gender = await ssrnet.loadGender(config);
|
||||
if (config.face.enabled && config.face.emotion.enabled && !models.emotion) models.emotion = await emotion.load(config);
|
||||
state = 'load';
|
||||
await load();
|
||||
|
||||
const perf = {};
|
||||
let timeStamp;
|
||||
|
||||
// run posenet
|
||||
timeStamp = now();
|
||||
tf.engine().startScope();
|
||||
|
||||
// run posenet
|
||||
state = 'run:body';
|
||||
timeStamp = now();
|
||||
const poseRes = config.body.enabled ? await models.posenet.estimatePoses(input, config.body) : [];
|
||||
tf.engine().endScope();
|
||||
perf.body = Math.trunc(now() - timeStamp);
|
||||
|
||||
// run handpose
|
||||
state = 'run:hand';
|
||||
timeStamp = now();
|
||||
tf.engine().startScope();
|
||||
const handRes = config.hand.enabled ? await models.handpose.estimateHands(input, config.hand) : [];
|
||||
tf.engine().endScope();
|
||||
perf.hand = Math.trunc(now() - timeStamp);
|
||||
|
||||
// run facemesh, includes blazeface and iris
|
||||
const faceRes = [];
|
||||
if (config.face.enabled) {
|
||||
state = 'run:face';
|
||||
timeStamp = now();
|
||||
tf.engine().startScope();
|
||||
const faces = await models.facemesh.estimateFaces(input, config.face);
|
||||
perf.face = Math.trunc(now() - timeStamp);
|
||||
for (const face of faces) {
|
||||
|
|
@ -129,10 +139,12 @@ async function detect(input, userConfig) {
|
|||
continue;
|
||||
}
|
||||
// run ssr-net age & gender, inherits face from blazeface
|
||||
state = 'run:agegender';
|
||||
timeStamp = now();
|
||||
const ssrData = (config.face.age.enabled || config.face.gender.enabled) ? await ssrnet.predict(face.image, config) : {};
|
||||
perf.agegender = Math.trunc(now() - timeStamp);
|
||||
// run emotion, inherits face from blazeface
|
||||
state = 'run:emotion';
|
||||
timeStamp = now();
|
||||
const emotionData = config.face.emotion.enabled ? await emotion.predict(face.image, config) : {};
|
||||
perf.emotion = Math.trunc(now() - timeStamp);
|
||||
|
|
@ -154,12 +166,14 @@ async function detect(input, userConfig) {
|
|||
iris: (iris !== 0) ? Math.trunc(100 * 11.7 /* human iris size in mm */ / iris) / 100 : 0,
|
||||
});
|
||||
}
|
||||
tf.engine().endScope();
|
||||
state = 'idle';
|
||||
}
|
||||
|
||||
// set depthwiseconv to original value
|
||||
// tf.env().set('WEBGL_PACK_DEPTHWISECONV', savedWebglPackDepthwiseConvFlag);
|
||||
|
||||
tf.engine().endScope();
|
||||
|
||||
// combine and return results
|
||||
perf.total = Object.values(perf).reduce((a, b) => a + b);
|
||||
resolve({ face: faceRes, body: poseRes, hand: handRes, performance: perf });
|
||||
|
|
@ -176,3 +190,4 @@ exports.posenet = posenet;
|
|||
exports.handpose = handpose;
|
||||
exports.tf = tf;
|
||||
exports.version = app.version;
|
||||
exports.state = state;
|
||||
|
|
|
|||
Loading…
Reference in New Issue