在浏览器和 Node.js 中使用 tensorflow.js 进行人脸检测和人脸识别的 JavaScript API。「JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js」
基于 tensorflow.js 内核实现的浏览器和 nodejs 的 JavaScript 人脸识别 API
在 tensorflow.js 核心（tensorflow/tfjs-core）上实现的针对浏览器和 Node.js 的 JavaScript 人脸识别 API。
（The first version translated by vz on 2020.07.18. To be continued）
| Name With Owner | justadudewhohacks/face-api.js |
|---|---|
| Primary Language | TypeScript |
| Program language | JavaScript (Language Count: 2) |
| Platform | Linux, Mac, Web browsers, Windows |
| License: | MIT License |
| Created At | 2018-05-19 06:14:53 |
|---|---|
| Pushed At | 2024-01-24 09:25:35 |
| Last Commit At | 2020-04-22 18:40:10 |
| Release Count | 38 |
| Last Release Name | 0.22.2 (Posted on ) |
| First Release Name | 0.0.0 (Posted on ) |
| Stargazers Count | 17.3k |
|---|---|
| Watchers Count | 345 |
| Fork Count | 3.8k |
| Commits Count | 488 |
| Has Issues Enabled | |
| Issues Count | 846 |
| Issue Open Count | 452 |
| Pull Requests Count | 71 |
| Pull Requests Open Count | 21 |
| Pull Requests Close Count | 19 |
| Has Wiki Enabled | |
|---|---|
| Is Archived | |
| Is Fork | |
| Is Locked | |
| Is Mirror | |
| Is Private |
JavaScript face recognition API for the browser and nodejs implemented on top of tensorflow.js core (tensorflow/tfjs-core)
Clone the repository:
git clone https://github.com/justadudewhohacks/face-api.js.git
cd face-api.js/examples/examples-browser
npm i
npm start
Browse to http://localhost:3000/.
cd face-api.js/examples/examples-nodejs
npm i
Now run one of the examples using ts-node:
ts-node faceDetection.ts
Or simply compile and run them with node:
tsc faceDetection.ts
node faceDetection.js
Simply include the latest script from dist/face-api.js.
Or install it via npm:
npm i face-api.js
We can use the equivalent API in a nodejs environment by polyfilling some browser specifics, such as HTMLImageElement, HTMLCanvasElement and ImageData. The easiest way to do so is by installing the node-canvas package.
Alternatively you can simply construct your own tensors from image data and pass tensors as inputs to the API.
Furthermore you want to install @tensorflow/tfjs-node (not required, but highly recommended), which speeds things up drastically by compiling and binding to the native Tensorflow C++ library:
npm i face-api.js canvas @tensorflow/tfjs-node
Now we simply monkey patch the environment to use the polyfills:
// import nodejs bindings to native tensorflow,
// not required, but will speed up things drastically (python required)
import '@tensorflow/tfjs-node';
// implements nodejs wrappers for HTMLCanvasElement, HTMLImageElement, ImageData
import * as canvas from 'canvas';
import * as faceapi from 'face-api.js';
// patch nodejs environment, we need to provide an implementation of
// HTMLCanvasElement and HTMLImageElement
const { Canvas, Image, ImageData } = canvas
faceapi.env.monkeyPatch({ Canvas, Image, ImageData })
All global neural network instances are exported via faceapi.nets:
console.log(faceapi.nets)
// ageGenderNet
// faceExpressionNet
// faceLandmark68Net
// faceLandmark68TinyNet
// faceRecognitionNet
// ssdMobilenetv1
// tinyFaceDetector
// tinyYolov2
To load a model, you have to provide the corresponding manifest.json file as well as the model weight files (shards) as assets. Simply copy them to your public or assets folder. The manifest.json and shard files of a model have to be located in the same directory / accessible under the same route.
Assuming the models reside in public/models:
await faceapi.nets.ssdMobilenetv1.loadFromUri('/models')
// accordingly for the other models:
// await faceapi.nets.faceLandmark68Net.loadFromUri('/models')
// await faceapi.nets.faceRecognitionNet.loadFromUri('/models')
// ...
In a nodejs environment you can furthermore load the models directly from disk:
await faceapi.nets.ssdMobilenetv1.loadFromDisk('./models')
You can also load the model from a tf.NamedTensorMap:
await faceapi.nets.ssdMobilenetv1.loadFromWeightMap(weightMap)
Alternatively, you can also create own instances of the neural nets:
const net = new faceapi.SsdMobilenetv1()
await net.loadFromUri('/models')
You can also load the weights as a Float32Array (in case you want to use the uncompressed models):
// using fetch
net.load(await faceapi.fetchNetWeights('/models/face_detection_model.weights'))
// using axios
const res = await axios.get('/models/face_detection_model.weights', { responseType: 'arraybuffer' })
const weights = new Float32Array(res.data)
net.load(weights)
In the following input can be an HTML img, video or canvas element or the id of that element.
<img id="myImg" src="images/example.png" />
<video id="myVideo" src="media/example.mp4" />
<canvas id="myCanvas" />
const input = document.getElementById('myImg')
// const input = document.getElementById('myVideo')
// const input = document.getElementById('myCanvas')
// or simply:
// const input = 'myImg'
Detect all faces in an image. Returns Array<FaceDetection>:
const detections = await faceapi.detectAllFaces(input)
Detect the face with the highest confidence score in an image. Returns **FaceDetection