mirror of https://github.com/vladmandic/human
update demos
Vladimir Mandic
parent
41b5880bf0
commit
185d129a17
1
Demos.md
1
Demos.md
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@ -9,6 +9,7 @@ All demos are included in `/demo` and come with individual documentation per-dem
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- **Full** [[*Live*]](https://vladmandic.github.io/human/demo/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo): Main browser demo app that showcases all Human capabilities
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- **Simple** [[*Live*]](https://vladmandic.github.io/human/demo/typescript/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/typescript): Simple demo in WebCam processing demo in TypeScript
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- **Embedded** [[*Live*]](https://vladmandic.github.io/human/demo/video/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/video/index.html): Even simpler demo with tiny code embedded in HTML file
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- **Face Detect** [[*Live*]](https://vladmandic.github.io/human/demo/facedetect/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facedetect): Extract faces from images and processes details
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- **Face Match** [[*Live*]](https://vladmandic.github.io/human/demo/facematch/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch): Extract faces from images, calculates face descriptors and similarities and matches them to known database
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- **Face ID** [[*Live*]](https://vladmandic.github.io/human/demo/faceid/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/faceid): Runs multiple checks to validate webcam input before performing face match to faces in IndexDB
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- **Multi-thread** [[*Live*]](https://vladmandic.github.io/human/demo/multithread/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/multithread): Runs each Human module in a separate web worker for highest possible performance
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266
Home.md
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Home.md
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@ -14,35 +14,6 @@
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<br>
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## Highlights
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- Compatible with most server-side and client-side environments and frameworks
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- Combines multiple machine learning models which can be switched on-demand depending on the use-case
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- Related models are executed in an attention pipeline to provide details when needed
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- Optimized input pre-processing that can enhance image quality of any type of inputs
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- Detection of frame changes to trigger only required models for improved performance
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- Intelligent temporal interpolation to provide smooth results regardless of processing performance
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- Simple unified API
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- Built-in Image, Video and WebCam handling
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[*Jump to Quick Start*](#quick-start)
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<br>
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## Compatibility
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- **Browser**:
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Compatible with both desktop and mobile platforms
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Compatible with *CPU*, *WebGL*, *WASM* backends
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Compatible with *WebWorker* execution
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Compatible with *WebView*
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- **NodeJS**:
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Compatibile with *WASM* backend for executions on architectures where *tensorflow* binaries are not available
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Compatible with *tfjs-node* using software execution via *tensorflow* shared libraries
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Compatible with *tfjs-node* using GPU-accelerated execution via *tensorflow* shared libraries and nVidia CUDA
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<br>
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## Releases
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- [Release Notes](https://github.com/vladmandic/human/releases)
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- [NPM Link](https://www.npmjs.com/package/@vladmandic/human)
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@ -70,6 +41,7 @@
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- **Full** [[*Live*]](https://vladmandic.github.io/human/demo/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo): Main browser demo app that showcases all Human capabilities
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- **Simple** [[*Live*]](https://vladmandic.github.io/human/demo/typescript/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/typescript): Simple demo in WebCam processing demo in TypeScript
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- **Embedded** [[*Live*]](https://vladmandic.github.io/human/demo/video/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/video/index.html): Even simpler demo with tiny code embedded in HTML file
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- **Face Detect** [[*Live*]](https://vladmandic.github.io/human/demo/facedetect/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facedetect): Extract faces from images and processes details
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- **Face Match** [[*Live*]](https://vladmandic.github.io/human/demo/facematch/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch): Extract faces from images, calculates face descriptors and similarities and matches them to known database
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- **Face ID** [[*Live*]](https://vladmandic.github.io/human/demo/faceid/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/faceid): Runs multiple checks to validate webcam input before performing face match to faces in IndexDB
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- **Multi-thread** [[*Live*]](https://vladmandic.github.io/human/demo/multithread/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/multithread): Runs each Human module in a separate web worker for highest possible performance
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@ -140,240 +112,4 @@
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*Suggestions are welcome!*
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<br><hr><br>
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## Quick Start
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Simply load `Human` (*IIFE version*) directly from a cloud CDN in your HTML file:
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(pick one: `jsdelirv`, `unpkg` or `cdnjs`)
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```html
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<!DOCTYPE HTML>
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<script src="https://cdn.jsdelivr.net/npm/@vladmandic/human/dist/human.js"></script>
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<script src="https://unpkg.dev/@vladmandic/human/dist/human.js"></script>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/human/3.0.0/human.js"></script>
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```
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For details, including how to use `Browser ESM` version or `NodeJS` version of `Human`, see [**Installation**](https://github.com/vladmandic/human/wiki/Install)
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<br>
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## Code Examples
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Simple app that uses Human to process video input and
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draw output on screen using internal draw helper functions
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```js
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// create instance of human with simple configuration using default values
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const config = { backend: 'webgl' };
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const human = new Human(config);
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// select input HTMLVideoElement and output HTMLCanvasElement from page
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const inputVideo = document.getElementById('video-id');
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const outputCanvas = document.getElementById('canvas-id');
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function detectVideo() {
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// perform processing using default configuration
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human.detect(inputVideo).then((result) => {
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// result object will contain detected details
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// as well as the processed canvas itself
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// so lets first draw processed frame on canvas
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human.draw.canvas(result.canvas, outputCanvas);
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// then draw results on the same canvas
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human.draw.face(outputCanvas, result.face);
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human.draw.body(outputCanvas, result.body);
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human.draw.hand(outputCanvas, result.hand);
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human.draw.gesture(outputCanvas, result.gesture);
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// and loop immediate to the next frame
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requestAnimationFrame(detectVideo);
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return result;
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});
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}
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detectVideo();
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```
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or using `async/await`:
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```js
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// create instance of human with simple configuration using default values
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const config = { backend: 'webgl' };
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const human = new Human(config); // create instance of Human
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const inputVideo = document.getElementById('video-id');
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const outputCanvas = document.getElementById('canvas-id');
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async function detectVideo() {
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const result = await human.detect(inputVideo); // run detection
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human.draw.all(outputCanvas, result); // draw all results
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requestAnimationFrame(detectVideo); // run loop
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}
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detectVideo(); // start loop
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```
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or using `Events`:
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```js
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// create instance of human with simple configuration using default values
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const config = { backend: 'webgl' };
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const human = new Human(config); // create instance of Human
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const inputVideo = document.getElementById('video-id');
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const outputCanvas = document.getElementById('canvas-id');
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human.events.addEventListener('detect', () => { // event gets triggered when detect is complete
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human.draw.all(outputCanvas, human.result); // draw all results
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});
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function detectVideo() {
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human.detect(inputVideo) // run detection
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.then(() => requestAnimationFrame(detectVideo)); // upon detect complete start processing of the next frame
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}
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detectVideo(); // start loop
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```
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or using interpolated results for smooth video processing by separating detection and drawing loops:
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```js
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const human = new Human(); // create instance of Human
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const inputVideo = document.getElementById('video-id');
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const outputCanvas = document.getElementById('canvas-id');
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let result;
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async function detectVideo() {
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result = await human.detect(inputVideo); // run detection
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requestAnimationFrame(detectVideo); // run detect loop
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}
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async function drawVideo() {
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if (result) { // check if result is available
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const interpolated = human.next(result); // get smoothened result using last-known results
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human.draw.all(outputCanvas, interpolated); // draw the frame
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}
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requestAnimationFrame(drawVideo); // run draw loop
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}
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detectVideo(); // start detection loop
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drawVideo(); // start draw loop
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```
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or same, but using built-in full video processing instead of running manual frame-by-frame loop:
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```js
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const human = new Human(); // create instance of Human
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const inputVideo = document.getElementById('video-id');
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const outputCanvas = document.getElementById('canvas-id');
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async function drawResults() {
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const interpolated = human.next(); // get smoothened result using last-known results
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human.draw.all(outputCanvas, interpolated); // draw the frame
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requestAnimationFrame(drawResults); // run draw loop
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}
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human.video(inputVideo); // start detection loop which continously updates results
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drawResults(); // start draw loop
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```
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or using built-in webcam helper methods that take care of video handling completely:
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```js
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const human = new Human(); // create instance of Human
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const outputCanvas = document.getElementById('canvas-id');
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async function drawResults() {
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const interpolated = human.next(); // get smoothened result using last-known results
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human.draw.canvas(outputCanvas, human.webcam.element); // draw current webcam frame
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human.draw.all(outputCanvas, interpolated); // draw the frame detectgion results
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requestAnimationFrame(drawResults); // run draw loop
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}
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await human.webcam.start({ crop: true });
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human.video(human.webcam.element); // start detection loop which continously updates results
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drawResults(); // start draw loop
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```
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And for even better results, you can run detection in a separate web worker thread
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<br><hr><br>
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## Inputs
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`Human` library can process all known input types:
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- `Image`, `ImageData`, `ImageBitmap`, `Canvas`, `OffscreenCanvas`, `Tensor`,
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- `HTMLImageElement`, `HTMLCanvasElement`, `HTMLVideoElement`, `HTMLMediaElement`
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Additionally, `HTMLVideoElement`, `HTMLMediaElement` can be a standard `<video>` tag that links to:
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- WebCam on user's system
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- Any supported video type
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e.g. `.mp4`, `.avi`, etc.
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- Additional video types supported via *HTML5 Media Source Extensions*
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e.g.: **HLS** (*HTTP Live Streaming*) using `hls.js` or **DASH** (*Dynamic Adaptive Streaming over HTTP*) using `dash.js`
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- **WebRTC** media track using built-in support
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<br><hr><br>
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## Detailed Usage
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- [**Wiki Home**](https://github.com/vladmandic/human/wiki)
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- [**List of all available methods, properies and namespaces**](https://github.com/vladmandic/human/wiki/Usage)
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- [**TypeDoc API Specification - Main class**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
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- [**TypeDoc API Specification - Full**](https://vladmandic.github.io/human/typedoc/)
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<br><hr><br>
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## TypeDefs
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`Human` is written using TypeScript strong typing and ships with full **TypeDefs** for all classes defined by the library bundled in `types/human.d.ts` and enabled by default
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*Note*: This does not include embedded `tfjs`
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If you want to use embedded `tfjs` inside `Human` (`human.tf` namespace) and still full **typedefs**, add this code:
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> import type * as tfjs from '@vladmandic/human/dist/tfjs.esm';
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> const tf = human.tf as typeof tfjs;
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This is not enabled by default as `Human` does not ship with full **TFJS TypeDefs** due to size considerations
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Enabling `tfjs` TypeDefs as above creates additional project (dev-only as only types are required) dependencies as defined in `@vladmandic/human/dist/tfjs.esm.d.ts`:
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> @tensorflow/tfjs-core, @tensorflow/tfjs-converter, @tensorflow/tfjs-backend-wasm, @tensorflow/tfjs-backend-webgl
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<br><hr><br>
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## Default models
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Default models in Human library are:
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- **Face Detection**: *MediaPipe BlazeFace Back variation*
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- **Face Mesh**: *MediaPipe FaceMesh*
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- **Face Iris Analysis**: *MediaPipe Iris*
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- **Face Description**: *HSE FaceRes*
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- **Emotion Detection**: *Oarriaga Emotion*
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- **Body Analysis**: *MoveNet Lightning variation*
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- **Hand Analysis**: *HandTrack & MediaPipe HandLandmarks*
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- **Body Segmentation**: *Google Selfie*
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- **Object Detection**: *CenterNet with MobileNet v3*
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Note that alternative models are provided and can be enabled via configuration
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For example, body pose detection by default uses *MoveNet Lightning*, but can be switched to *MultiNet Thunder* for higher precision or *Multinet MultiPose* for multi-person detection or even *PoseNet*, *BlazePose* or *EfficientPose* depending on the use case
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For more info, see [**Configuration Details**](https://github.com/vladmandic/human/wiki/Configuration) and [**List of Models**](https://github.com/vladmandic/human/wiki/Models)
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<br><hr><br>
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## Diagnostics
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- [How to get diagnostic information or performance trace information](https://github.com/vladmandic/human/wiki/Diag)
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<br><hr><br>
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`Human` library is written in [TypeScript](https://www.typescriptlang.org/docs/handbook/intro.html) **4.9** using [TensorFlow/JS](https://www.tensorflow.org/js/) **4.1** and conforming to latest `JavaScript` [ECMAScript version 2022](https://262.ecma-international.org/) standard
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Build target for distributables is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/9.0/)
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<br>
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For details see [**Wiki Pages**](https://github.com/vladmandic/human/wiki)
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and [**API Specification**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
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<br>
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