updated demos
Vladimir Mandic
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b11d5406bc
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README.md
106
README.md
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@ -4,10 +4,7 @@
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This is updated **face-api.js** with latest available TensorFlow/JS as the original face-api.js is not compatible with **tfjs 2.0+**.
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Forked from **face-api.js** version **0.22.2** released on March 22nd, 2020
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- <https://github.com/justadudewhohacks/face-api.js>
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- <https://www.npmjs.com/package/face-api.js>
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Forked from [face-api.js](https://github.com/justadudewhohacks/face-api.js) version **0.22.2** released on March 22nd, 2020
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Currently based on **`TensorFlow/JS` 2.7.0**
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@ -18,6 +15,8 @@ And since original Face-API was open-source, I've released this version as well
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Unfortunately, changes ended up being too large for a simple pull request on original Face-API and it ended up being a full-fledged version on its own
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<br>
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### Differences
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- Compatible with `TensorFlow/JS 2.0+`
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@ -41,6 +40,73 @@ Unfortunately, changes ended up being too large for a simple pull request on ori
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Which means valid models are **tinyFaceDetector** and **mobileNetv1**
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<br>
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<hr>
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<br>
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## Examples
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<br>
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### Browser
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Browser example that uses both models as well as all of the extensions is included in `/example/index.html`
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Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/>
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<br>
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*Note: Photos shown below are taken by me*
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<br>
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### NodeJS
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Two NodeJS examples are:
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- `/example/node-singleprocess.js`: Regular usage of `FaceAPI` from `NodeJS`
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- `/example/node-multiprocess.js`: Multiprocessing showcase that uses pool of worker processes (`node-multiprocess-worker.js`
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Main starts fixed pool of worker processes with each worker having it's instance of `FaceAPI`
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Workers communicate with main when they are ready and main dispaches job to each ready worker until job queue is empty
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```json
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2020-12-08 08:30:01 INFO: @vladmandic/face-api version 0.9.1
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2020-12-08 08:30:01 INFO: User: vlado Platform: linux Arch: x64 Node: v15.0.1
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2020-12-08 08:30:01 INFO: FaceAPI multi-process test
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2020-12-08 08:30:01 STATE: Main: started worker: 265238
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2020-12-08 08:30:01 STATE: Main: started worker: 265244
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2020-12-08 08:30:02 STATE: Worker: PID: 265238 TensorFlow/JS 2.7.0 FaceAPI 0.9.1 Backend: tensorflow
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2020-12-08 08:30:02 STATE: Worker: PID: 265244 TensorFlow/JS 2.7.0 FaceAPI 0.9.1 Backend: tensorflow
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2020-12-08 08:30:02 STATE: Main: dispatching to worker: 265238
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2020-12-08 08:30:02 STATE: Main: dispatching to worker: 265244
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2020-12-08 08:30:02 DATA: Worker received message: 265238 { image: 'example/sample (1).jpg' }
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2020-12-08 08:30:02 DATA: Worker received message: 265244 { image: 'example/sample (2).jpg' }
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2020-12-08 08:30:04 DATA: Main: worker finished: 265238 detected faces: 3
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2020-12-08 08:30:04 STATE: Main: dispatching to worker: 265238
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2020-12-08 08:30:04 DATA: Main: worker finished: 265244 detected faces: 3
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2020-12-08 08:30:04 STATE: Main: dispatching to worker: 265244
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2020-12-08 08:30:04 DATA: Worker received message: 265238 { image: 'example/sample (3).jpg' }
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2020-12-08 08:30:04 DATA: Worker received message: 265244 { image: 'example/sample (4).jpg' }
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2020-12-08 08:30:06 DATA: Main: worker finished: 265238 detected faces: 3
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2020-12-08 08:30:06 STATE: Main: dispatching to worker: 265238
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2020-12-08 08:30:06 DATA: Worker received message: 265238 { image: 'example/sample (5).jpg' }
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2020-12-08 08:30:06 DATA: Main: worker finished: 265244 detected faces: 4
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2020-12-08 08:30:06 STATE: Main: dispatching to worker: 265244
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2020-12-08 08:30:06 DATA: Worker received message: 265244 { image: 'example/sample (6).jpg' }
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2020-12-08 08:30:07 DATA: Main: worker finished: 265238 detected faces: 5
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2020-12-08 08:30:07 STATE: Main: worker exit: 265238 0
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2020-12-08 08:30:08 DATA: Main: worker finished: 265244 detected faces: 4
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2020-12-08 08:30:08 INFO: Processed 12 images in 6826 ms
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2020-12-08 08:30:08 STATE: Main: worker exit: 265244 0
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```
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Note that `@tensorflow/tfjs-node` or `@tensorflow/tfjs-node-gpu` must be installed before using NodeJS example
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<br>
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<hr>
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<br>
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## Installation
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Face-API ships with several pre-build versions of the library:
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@ -68,6 +134,8 @@ Reason for additional `nobundle` version is if you want to include a specific ve
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All versions include `sourcemap` and `asset manifest`
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<br>
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<hr>
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<br>
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There are several ways to use Face-API:
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@ -171,10 +239,16 @@ And then use with:
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const faceapi = require('@vladmandic/face-api/dist/face-api.node-gpu.js'); // this loads face-api version with correct bindings for tfjs-node-gpu
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```
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<br>
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<hr>
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<br>
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## Weights
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Pretrained models and their weights are includes in `./model`.
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<br>
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## Build
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If you want to do a full rebuild, either download npm module
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@ -218,6 +292,8 @@ npm run build
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2020-12-02 16:31:25 STATE: Build for: browserBundle type: esm: { imports: 162, importBytes: 1728673, modules: 576, moduleBytes: 1359851, outputBytes: 1900836, outputFiles: 'dist/face-api.esm.js' }
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```
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<br>
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<hr>
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<br>
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## Credits & Documentation
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@ -225,25 +301,3 @@ npm run build
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- Original project and usage documentation: [Face-API](https://github.com/justadudewhohacks/face-api.js)
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- Original model weighs: [Face-API](https://github.com/justadudewhohacks/face-api.js-models)
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- ML API Documentation: [Tensorflow/JS](https://js.tensorflow.org/api/latest/)
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<br>
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## Example
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<br>
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### Browser
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Example that uses both models as well as all of the extensions is included in `/example/index.html`
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Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/>
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<br>
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### NodeJS
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Example is included in `/example/node.js`
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Note that it does not require any other other 3rd party libraries
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*Note: Photos shown below are taken by me*
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"src/index.ts": {
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const fs = require('fs');
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const path = require('path');
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const log = require('@vladmandic/pilogger');
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// workers actual import tfjs and faceapi modules
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const tf = require('@tensorflow/tfjs-node');
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const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'
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// options used by faceapi
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const modelPathRoot = '../model';
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const minScore = 0.1;
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const maxResults = 5;
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let optionsSSDMobileNet;
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// read image from a file and create tensor to be used by faceapi
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// this way we don't need any monkey patches
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// you can add any pre-proocessing here such as resizing, etc.
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async function image(img) {
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const buffer = fs.readFileSync(img);
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const tensor = tf.tidy(() => tf.node.decodeImage(buffer).toFloat().expandDims());
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return tensor;
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}
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// actual faceapi detection
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async function detect(img) {
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const tensor = await image(img);
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const result = await faceapi
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.detectAllFaces(tensor, optionsSSDMobileNet)
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.withFaceLandmarks()
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.withFaceExpressions()
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.withFaceDescriptors()
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.withAgeAndGender();
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process.send({ image: img, detected: result }); // send results back to main
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process.send({ ready: true }); // send signal back to main that this worker is now idle and ready for next image
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tensor.dispose();
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}
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async function main() {
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// on worker start first initialize message handler so we don't miss any messages
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process.on('message', (msg) => {
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if (msg.exit) process.exit(); // if main told worker to exit
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if (msg.test) process.send({ test: true });
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if (msg.image) detect(msg.image); // if main told worker to process image
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log.data('Worker received message:', process.pid, msg); // generic log
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});
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// then initialize tfjs
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await faceapi.tf.setBackend('tensorflow');
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await faceapi.tf.enableProdMode();
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await faceapi.tf.ENV.set('DEBUG', false);
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await faceapi.tf.ready();
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log.state('Worker: PID:', process.pid, `TensorFlow/JS ${faceapi.tf.version_core} FaceAPI ${faceapi.version.faceapi} Backend: ${faceapi.tf.getBackend()}`);
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// and load and initialize facepi models
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const modelPath = path.join(__dirname, modelPathRoot);
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await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelPath);
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await faceapi.nets.ageGenderNet.loadFromDisk(modelPath);
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await faceapi.nets.faceLandmark68Net.loadFromDisk(modelPath);
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await faceapi.nets.faceRecognitionNet.loadFromDisk(modelPath);
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await faceapi.nets.faceExpressionNet.loadFromDisk(modelPath);
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optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
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// now we're ready, so send message back to main that it knows it can use this worker
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process.send({ ready: true });
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}
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main();
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const fs = require('fs');
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const path = require('path');
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const log = require('@vladmandic/pilogger'); // this is my simple logger with few extra features
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const child_process = require('child_process');
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// note that main process import faceapi or tfjs at all
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const imgPathRoot = './example'; // modify to include your sample images
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const numWorkers = 2; // how many workers will be started
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const workers = []; // this holds worker processes
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const images = []; // this holds queue of enumerated images
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const t = []; // timers
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let dir;
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// trigered by main when worker sends ready message
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// if image pool is empty, signal worker to exit otherwise dispatch image to worker and remove image from queue
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async function detect(worker) {
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if (!t[2]) t[2] = process.hrtime.bigint(); // first time do a timestamp so we can measure initial latency
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if (images.length === dir.length) worker.send({ test: true }); // for first image in queue just measure latency
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if (images.length === 0) worker.send({ exit: true }); // nothing left in queue
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else {
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log.state('Main: dispatching to worker:', worker.pid);
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worker.send({ image: images[0] });
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images.shift();
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}
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}
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// loop that waits for all workers to complete
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function waitCompletion() {
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const activeWorkers = workers.reduce((any, worker) => (any += worker.connected ? 1 : 0), 0);
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if (activeWorkers > 0) setImmediate(() => waitCompletion());
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else {
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t[1] = process.hrtime.bigint();
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log.info('Processed', dir.length, 'images in', Math.trunc(parseInt(t[1] - t[0]) / 1000 / 1000), 'ms');
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}
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}
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function measureLatency() {
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t[3] = process.hrtime.bigint();
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const latencyInitialization = Math.trunc(parseInt(t[2] - t[0]) / 1000 / 1000);
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const latencyRoundTrip = Math.trunc(parseInt(t[3] - t[2]) / 1000 / 1000);
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log.info('Latency: worker initializtion: ', latencyInitialization, 'message round trip:', latencyRoundTrip);
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}
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async function main() {
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log.header();
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log.info('FaceAPI multi-process test');
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// enumerate all images into queue
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dir = fs.readdirSync(imgPathRoot);
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for (const imgFile of dir) {
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if (imgFile.toLocaleLowerCase().endsWith('.jpg')) images.push(path.join(imgPathRoot, imgFile));
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}
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t[0] = process.hrtime.bigint();
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// manage worker processes
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for (let i = 0; i < numWorkers; i++) {
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// create worker process
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workers[i] = await child_process.fork('example/node-multiprocess-worker.js', ['special']);
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// parse message that worker process sends back to main
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// if message is ready, dispatch next image in queue
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// if message is processing result, just print how many faces were detected
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// otherwise it's an unknown message
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workers[i].on('message', (msg) => {
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if (msg.ready) detect(workers[i]);
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else if (msg.image) log.data('Main: worker finished:', workers[i].pid, 'detected faces:', msg.detected.length);
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else if (msg.test) measureLatency();
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else log.data('Main: worker message:', workers[i].pid, msg);
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});
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// just log when worker exits
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workers[i].on('exit', (msg) => log.state('Main: worker exit:', workers[i].pid, msg));
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// just log which worker was started
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log.state('Main: started worker:', workers[i].pid);
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}
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// wait for all workers to complete
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waitCompletion();
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}
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main();
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const fs = require('fs');
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const path = require('path');
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const log = require('@vladmandic/pilogger');
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const tf = require('@tensorflow/tfjs-node');
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const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'
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const modelPathRoot = '../model';
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const imgPathRoot = './example'; // modify to include your sample images
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const minScore = 0.1;
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const maxResults = 5;
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async function image(img) {
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const buffer = fs.readFileSync(img);
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const decoded = tf.node.decodeImage(buffer);
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const casted = decoded.toFloat();
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const result = casted.expandDims(0);
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decoded.dispose();
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casted.dispose();
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return result;
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}
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async function main() {
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log.header();
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log.info('FaceAPI single-process test');
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const t0 = process.hrtime.bigint();
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await faceapi.tf.setBackend('tensorflow');
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await faceapi.tf.enableProdMode();
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await faceapi.tf.ENV.set('DEBUG', false);
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await faceapi.tf.ready();
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log.state(`Version: TensorFlow/JS ${faceapi.tf?.version_core} FaceAPI ${faceapi.version.faceapi} Backend: ${faceapi.tf?.getBackend()}`);
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log.info('Loading FaceAPI models');
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const modelPath = path.join(__dirname, modelPathRoot);
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await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelPath);
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await faceapi.nets.ageGenderNet.loadFromDisk(modelPath);
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await faceapi.nets.faceLandmark68Net.loadFromDisk(modelPath);
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await faceapi.nets.faceRecognitionNet.loadFromDisk(modelPath);
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await faceapi.nets.faceExpressionNet.loadFromDisk(modelPath);
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const optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
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const dir = fs.readdirSync(imgPathRoot);
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for (const img of dir) {
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if (!img.toLocaleLowerCase().endsWith('.jpg')) continue;
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const tensor = await image(path.join(imgPathRoot, img));
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const result = await faceapi
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.detectAllFaces(tensor, optionsSSDMobileNet)
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.withFaceLandmarks()
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.withFaceExpressions()
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.withFaceDescriptors()
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.withAgeAndGender();
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log.data('Image:', img, 'Detected faces:', result.length);
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tensor.dispose();
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}
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const t1 = process.hrtime.bigint();
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log.info('Processed', dir.length, 'images in', Math.trunc(parseInt(t1 - t0) / 1000 / 1000), 'ms');
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}
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main();
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process.stderr.write = null; // silly hack to stock tfjs logging too much to stderr
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const fs = require('fs');
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const path = require('path');
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const tf = require('@tensorflow/tfjs-node');
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const faceapi = require('../dist/face-api.node.js');
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// if you have module installed, this would be
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// const faceapi = require('@vladmandic/face-api');
|
||||
|
||||
// configuration options
|
||||
const modelPathRoot = '../model/'; // path to model folder that will be loaded using http
|
||||
const imgSize = 512; // maximum image size in pixels
|
||||
const minScore = 0.1; // minimum score
|
||||
const maxResults = 5; // maximum number of results to return
|
||||
const samples = ['sample (1).jpg', 'sample (2).jpg', 'sample (3).jpg', 'sample (4).jpg', 'sample (5).jpg', 'sample (6).jpg']; // sample images to be loaded using http
|
||||
|
||||
// helper function to pretty-print json object to string
|
||||
function str(json) {
|
||||
const text = json ? JSON.stringify(json).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ') : '';
|
||||
return text;
|
||||
}
|
||||
|
||||
// helper function to print strings to html document as a log
|
||||
function log(...txt) {
|
||||
// eslint-disable-next-line no-console
|
||||
console.log(...txt);
|
||||
}
|
||||
|
||||
async function image(img) {
|
||||
const buffer = fs.readFileSync(img);
|
||||
const decoded = tf.node.decodeImage(buffer);
|
||||
const casted = decoded.toFloat();
|
||||
const result = casted.expandDims(0);
|
||||
decoded.dispose();
|
||||
casted.dispose();
|
||||
return result;
|
||||
}
|
||||
|
||||
async function main() {
|
||||
// initialize tfjs
|
||||
log('FaceAPI Test');
|
||||
await faceapi.tf.setBackend('tensorflow'); //Sets the backend (cpu, webgl, wasm, tensorflow, etc) responsible for creating tensors and executing operations on those tensors.
|
||||
await faceapi.tf.enableProdMode();
|
||||
await faceapi.tf.ENV.set('DEBUG', false);
|
||||
await faceapi.tf.ready(); //Returns a promise that resolves when the currently selected backend (or the highest priority one) has initialized.
|
||||
|
||||
// check version
|
||||
log(`Version: TensorFlow/JS ${str(faceapi.tf?.version_core || '(not loaded)')} FaceAPI ${str(faceapi?.version || '(not loaded)')} Backend: ${str(faceapi.tf?.getBackend() || '(not loaded)')}`);
|
||||
log(`Flags: ${JSON.stringify(faceapi.tf.ENV.flags)}`);
|
||||
|
||||
// load face-api models
|
||||
log('Loading FaceAPI models');
|
||||
const modelPath = path.join(__dirname, modelPathRoot);
|
||||
await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelPath);
|
||||
await faceapi.nets.ageGenderNet.loadFromDisk(modelPath);
|
||||
await faceapi.nets.faceLandmark68Net.loadFromDisk(modelPath);
|
||||
await faceapi.nets.faceRecognitionNet.loadFromDisk(modelPath);
|
||||
await faceapi.nets.faceExpressionNet.loadFromDisk(modelPath);
|
||||
const optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
|
||||
|
||||
// check tf engine state
|
||||
const engine = await faceapi.tf.engine();
|
||||
log(`TF Engine State: ${str(engine.state)}`);
|
||||
|
||||
const dir = fs.readdirSync(__dirname);
|
||||
for (const img of dir) {
|
||||
if (!img.toLocaleLowerCase().endsWith('.jpg')) continue;
|
||||
// load image
|
||||
const tensor = await image(path.join(__dirname, img));
|
||||
// actual model execution
|
||||
const result = await faceapi
|
||||
.detectAllFaces(tensor, optionsSSDMobileNet)
|
||||
.withFaceLandmarks()
|
||||
.withFaceExpressions()
|
||||
.withFaceDescriptors()
|
||||
.withAgeAndGender();
|
||||
log('Image:', img, 'Detected faces:', result.length);
|
||||
// you can access entire result object
|
||||
// console.log(result);
|
||||
tensor.dispose();
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
|
|
@ -196,8 +196,7 @@
|
|||
"@vladmandic/pilogger": {
|
||||
"version": "0.2.9",
|
||||
"resolved": "https://registry.npmjs.org/@vladmandic/pilogger/-/pilogger-0.2.9.tgz",
|
||||
"integrity": "sha512-UaDAFoEJwPw8248u9WQjVexP24wMiglHMWWd4X0gwukZuDw+CkoLddVF8335OYa+pXbP+t/rwx+E50f5rd5IhQ==",
|
||||
"dev": true
|
||||
"integrity": "sha512-UaDAFoEJwPw8248u9WQjVexP24wMiglHMWWd4X0gwukZuDw+CkoLddVF8335OYa+pXbP+t/rwx+E50f5rd5IhQ=="
|
||||
},
|
||||
"abbrev": {
|
||||
"version": "1.1.1",
|
||||
|
|
|
|||
10
package.json
10
package.json
|
|
@ -34,19 +34,19 @@
|
|||
"url": "https://github.com/vladmandic/face-api/issues"
|
||||
},
|
||||
"homepage": "https://github.com/vladmandic/face-api#readme",
|
||||
"Dependencies": {},
|
||||
"dependencies": {
|
||||
"@vladmandic/pilogger": "^0.2.9"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@tensorflow/tfjs": "^2.7.0",
|
||||
"@tensorflow/tfjs-backend-wasm": "^2.7.0",
|
||||
"@tensorflow/tfjs-node": "^2.7.0",
|
||||
"@tensorflow/tfjs-node-gpu": "^2.7.0",
|
||||
"@tensorflow/tfjs-backend-wasm": "^2.7.0",
|
||||
"@types/node": "^14.14.10",
|
||||
"@vladmandic/pilogger": "^0.2.9",
|
||||
"esbuild": "^0.8.17",
|
||||
"rimraf": "^3.0.2",
|
||||
"ts-node": "^9.0.0",
|
||||
"tslib": "^2.0.3",
|
||||
"typescript": "^4.1.2"
|
||||
},
|
||||
"dependencies": {}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue