updated demos

README.md

@@ -4,10 +4,7 @@
 
 This is updated **face-api.js** with latest available TensorFlow/JS as the original face-api.js is not compatible with **tfjs 2.0+**.  
 
-Forked from **face-api.js** version **0.22.2** released on March 22nd, 2020  
+Forked from [face-api.js](https://github.com/justadudewhohacks/face-api.js) version **0.22.2** released on March 22nd, 2020  
-
-- <https://github.com/justadudewhohacks/face-api.js>  
-- <https://www.npmjs.com/package/face-api.js>  
 
 Currently based on **`TensorFlow/JS` 2.7.0**  
 
@@ -18,6 +15,8 @@ And since original Face-API was open-source, I've released this version as well
 
 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  
 
+<br>
+
 ### Differences
 
 - Compatible with `TensorFlow/JS 2.0+`  
@@ -41,6 +40,73 @@ Unfortunately, changes ended up being too large for a simple pull request on ori
 
 Which means valid models are **tinyFaceDetector** and **mobileNetv1**  
 
+<br>
+<hr>
+<br>
+
+## Examples
+
+<br>
+
+### Browser
+
+Browser example that uses both models as well as all of the extensions is included in `/example/index.html`  
+Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/>  
+
+<br>
+
+*Note: Photos shown below are taken by me*
+
+![alt text](example/screenshot.png)
+
+<br>
+
+### NodeJS
+
+Two NodeJS examples are:
+
+- `/example/node-singleprocess.js`: Regular usage of `FaceAPI` from `NodeJS`  
+- `/example/node-multiprocess.js`: Multiprocessing showcase that uses pool of worker processes (`node-multiprocess-worker.js`
+  Main starts fixed pool of worker processes with each worker having it's instance of `FaceAPI`  
+  Workers communicate with main when they are ready and main dispaches job to each ready worker until job queue is empty  
+
+```json
+2020-12-08 08:30:01 INFO:  @vladmandic/face-api version 0.9.1
+2020-12-08 08:30:01 INFO:  User: vlado Platform: linux Arch: x64 Node: v15.0.1
+2020-12-08 08:30:01 INFO:  FaceAPI multi-process test
+2020-12-08 08:30:01 STATE:  Main: started worker: 265238
+2020-12-08 08:30:01 STATE:  Main: started worker: 265244
+2020-12-08 08:30:02 STATE:  Worker: PID: 265238 TensorFlow/JS 2.7.0 FaceAPI 0.9.1 Backend: tensorflow
+2020-12-08 08:30:02 STATE:  Worker: PID: 265244 TensorFlow/JS 2.7.0 FaceAPI 0.9.1 Backend: tensorflow
+2020-12-08 08:30:02 STATE:  Main: dispatching to worker: 265238
+2020-12-08 08:30:02 STATE:  Main: dispatching to worker: 265244
+2020-12-08 08:30:02 DATA:  Worker received message: 265238 { image: 'example/sample (1).jpg' }
+2020-12-08 08:30:02 DATA:  Worker received message: 265244 { image: 'example/sample (2).jpg' }
+2020-12-08 08:30:04 DATA:  Main: worker finished: 265238 detected faces: 3
+2020-12-08 08:30:04 STATE:  Main: dispatching to worker: 265238
+2020-12-08 08:30:04 DATA:  Main: worker finished: 265244 detected faces: 3
+2020-12-08 08:30:04 STATE:  Main: dispatching to worker: 265244
+2020-12-08 08:30:04 DATA:  Worker received message: 265238 { image: 'example/sample (3).jpg' }
+2020-12-08 08:30:04 DATA:  Worker received message: 265244 { image: 'example/sample (4).jpg' }
+2020-12-08 08:30:06 DATA:  Main: worker finished: 265238 detected faces: 3
+2020-12-08 08:30:06 STATE:  Main: dispatching to worker: 265238
+2020-12-08 08:30:06 DATA:  Worker received message: 265238 { image: 'example/sample (5).jpg' }
+2020-12-08 08:30:06 DATA:  Main: worker finished: 265244 detected faces: 4
+2020-12-08 08:30:06 STATE:  Main: dispatching to worker: 265244
+2020-12-08 08:30:06 DATA:  Worker received message: 265244 { image: 'example/sample (6).jpg' }
+2020-12-08 08:30:07 DATA:  Main: worker finished: 265238 detected faces: 5
+2020-12-08 08:30:07 STATE:  Main: worker exit: 265238 0
+2020-12-08 08:30:08 DATA:  Main: worker finished: 265244 detected faces: 4
+2020-12-08 08:30:08 INFO:  Processed 12 images in 6826 ms
+2020-12-08 08:30:08 STATE:  Main: worker exit: 265244 0
+```
+
+Note that `@tensorflow/tfjs-node` or `@tensorflow/tfjs-node-gpu` must be installed before using NodeJS example
+
+<br>
+<hr>
+<br>
+
 ## Installation
 
 Face-API ships with several pre-build versions of the library:
@@ -68,6 +134,8 @@ Reason for additional `nobundle` version is if you want to include a specific ve
 
 All versions include `sourcemap` and `asset manifest`
 
+<br>
+<hr>
 <br>
 
 There are several ways to use Face-API: 
@@ -171,10 +239,16 @@ And then use with:
   const faceapi = require('@vladmandic/face-api/dist/face-api.node-gpu.js'); // this loads face-api version with correct bindings for tfjs-node-gpu
 ```
 
+<br>
+<hr>
+<br>
+
 ## Weights
 
 Pretrained models and their weights are includes in `./model`.
 
+<br>
+
 ## Build
 
 If you want to do a full rebuild, either download npm module
@@ -218,6 +292,8 @@ npm run build
 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' }
 ```
 
+<br>
+<hr>
 <br>
 
 ## Credits & Documentation
@@ -225,25 +301,3 @@ npm run build
 - Original project and usage documentation: [Face-API](https://github.com/justadudewhohacks/face-api.js)  
 - Original model weighs: [Face-API](https://github.com/justadudewhohacks/face-api.js-models)
 - ML API Documentation: [Tensorflow/JS](https://js.tensorflow.org/api/latest/)
-
-<br>
-
-## Example
-
-<br>
-
-### Browser
-
-Example that uses both models as well as all of the extensions is included in `/example/index.html`  
-Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/>  
-
-<br>
-
-### NodeJS
-
-Example is included in `/example/node.js`  
-Note that it does not require any other other 3rd party libraries  
-
-*Note: Photos shown below are taken by me*
-
-![alt text](example/screenshot.png)

dist/face-api.esm-nobundle.json

@@ -2060,7 +2060,7 @@
       ]
     },
     "package.json": {
-      "bytes": 1409,
+      "bytes": 1352,
       "imports": []
     },
     "src/index.ts": {

dist/face-api.esm.json

@@ -13201,7 +13201,7 @@
       ]
     },
     "package.json": {
-      "bytes": 1409,
+      "bytes": 1352,
       "imports": []
     },
     "src/index.ts": {

dist/face-api.json

@@ -13201,7 +13201,7 @@
       ]
     },
     "package.json": {
-      "bytes": 1409,
+      "bytes": 1352,
       "imports": []
     },
     "src/index.ts": {

dist/face-api.node-gpu.json

@@ -2060,7 +2060,7 @@
       ]
     },
     "package.json": {
-      "bytes": 1409,
+      "bytes": 1352,
       "imports": []
     },
     "src/index.ts": {

dist/face-api.node.json

@@ -2060,7 +2060,7 @@
       ]
     },
     "package.json": {
-      "bytes": 1409,
+      "bytes": 1352,
       "imports": []
     },
     "src/index.ts": {

example/node-multiprocess-worker.js

@@ -0,0 +1,67 @@
+const fs = require('fs');
+const path = require('path');
+const log = require('@vladmandic/pilogger');
+
+// workers actual import tfjs and faceapi modules
+const tf = require('@tensorflow/tfjs-node');
+const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'
+
+// options used by faceapi
+const modelPathRoot = '../model';
+const minScore = 0.1;
+const maxResults = 5;
+let optionsSSDMobileNet;
+
+// read image from a file and create tensor to be used by faceapi
+// this way we don't need any monkey patches
+// you can add any pre-proocessing here such as resizing, etc.
+async function image(img) {
+  const buffer = fs.readFileSync(img);
+  const tensor = tf.tidy(() => tf.node.decodeImage(buffer).toFloat().expandDims());
+  return tensor;
+}
+
+// actual faceapi detection
+async function detect(img) {
+  const tensor = await image(img);
+  const result = await faceapi
+    .detectAllFaces(tensor, optionsSSDMobileNet)
+    .withFaceLandmarks()
+    .withFaceExpressions()
+    .withFaceDescriptors()
+    .withAgeAndGender();
+  process.send({ image: img, detected: result }); // send results back to main
+  process.send({ ready: true }); // send signal back to main that this worker is now idle and ready for next image
+  tensor.dispose();
+}
+
+async function main() {
+  // on worker start first initialize message handler so we don't miss any messages
+  process.on('message', (msg) => {
+    if (msg.exit) process.exit(); // if main told worker to exit
+    if (msg.test) process.send({ test: true });
+    if (msg.image) detect(msg.image); // if main told worker to process image
+    log.data('Worker received message:', process.pid, msg); // generic log
+  });
+
+  // then initialize tfjs
+  await faceapi.tf.setBackend('tensorflow');
+  await faceapi.tf.enableProdMode();
+  await faceapi.tf.ENV.set('DEBUG', false);
+  await faceapi.tf.ready();
+  log.state('Worker: PID:', process.pid, `TensorFlow/JS ${faceapi.tf.version_core} FaceAPI ${faceapi.version.faceapi} Backend: ${faceapi.tf.getBackend()}`);
+
+  // and load and initialize facepi 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);
+  optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
+
+  // now we're ready, so send message back to main that it knows it can use this worker
+  process.send({ ready: true });
+}
+
+main();

example/node-multiprocess.js

@@ -0,0 +1,79 @@
+const fs = require('fs');
+const path = require('path');
+const log = require('@vladmandic/pilogger'); // this is my simple logger with few extra features
+const child_process = require('child_process');
+// note that main process import faceapi or tfjs at all
+
+const imgPathRoot = './example'; // modify to include your sample images
+const numWorkers = 2; // how many workers will be started
+const workers = []; // this holds worker processes
+const images = []; // this holds queue of enumerated images
+const t = []; // timers
+let dir;
+
+// trigered by main when worker sends ready message
+// if image pool is empty, signal worker to exit otherwise dispatch image to worker and remove image from queue
+async function detect(worker) {
+  if (!t[2]) t[2] = process.hrtime.bigint(); // first time do a timestamp so we can measure initial latency
+  if (images.length === dir.length) worker.send({ test: true }); // for first image in queue just measure latency
+  if (images.length === 0) worker.send({ exit: true }); // nothing left in queue
+  else {
+    log.state('Main: dispatching to worker:', worker.pid);
+    worker.send({ image: images[0] });
+    images.shift();
+  }
+}
+
+// loop that waits for all workers to complete
+function waitCompletion() {
+  const activeWorkers = workers.reduce((any, worker) => (any += worker.connected ? 1 : 0), 0);
+  if (activeWorkers > 0) setImmediate(() => waitCompletion());
+  else {
+    t[1] = process.hrtime.bigint();
+    log.info('Processed', dir.length, 'images in', Math.trunc(parseInt(t[1] - t[0]) / 1000 / 1000), 'ms');
+  }
+}
+
+function measureLatency() {
+  t[3] = process.hrtime.bigint();
+  const latencyInitialization = Math.trunc(parseInt(t[2] - t[0]) / 1000 / 1000);
+  const latencyRoundTrip = Math.trunc(parseInt(t[3] - t[2]) / 1000 / 1000);
+  log.info('Latency: worker initializtion: ', latencyInitialization, 'message round trip:', latencyRoundTrip);
+}
+
+async function main() {
+  log.header();
+  log.info('FaceAPI multi-process test');
+
+  // enumerate all images into queue
+  dir = fs.readdirSync(imgPathRoot);
+  for (const imgFile of dir) {
+    if (imgFile.toLocaleLowerCase().endsWith('.jpg')) images.push(path.join(imgPathRoot, imgFile));
+  }
+
+  t[0] = process.hrtime.bigint();
+  // manage worker processes
+  for (let i = 0; i < numWorkers; i++) {
+    // create worker process
+    workers[i] = await child_process.fork('example/node-multiprocess-worker.js', ['special']);
+    // parse message that worker process sends back to main
+    // if message is ready, dispatch next image in queue
+    // if message is processing result, just print how many faces were detected
+    // otherwise it's an unknown message
+    workers[i].on('message', (msg) => {
+      if (msg.ready) detect(workers[i]);
+      else if (msg.image) log.data('Main: worker finished:', workers[i].pid, 'detected faces:', msg.detected.length);
+      else if (msg.test) measureLatency();
+      else log.data('Main: worker message:', workers[i].pid, msg);
+    });
+    // just log when worker exits
+    workers[i].on('exit', (msg) => log.state('Main: worker exit:', workers[i].pid, msg));
+    // just log which worker was started
+    log.state('Main: started worker:', workers[i].pid);
+  }
+
+  // wait for all workers to complete
+  waitCompletion();
+}
+
+main();

example/node-singleprocess.js

@@ -0,0 +1,60 @@
+const fs = require('fs');
+const path = require('path');
+const log = require('@vladmandic/pilogger');
+const tf = require('@tensorflow/tfjs-node');
+const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'
+
+const modelPathRoot = '../model';
+const imgPathRoot = './example'; // modify to include your sample images
+const minScore = 0.1;
+const maxResults = 5;
+
+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() {
+  log.header();
+  log.info('FaceAPI single-process test');
+  const t0 = process.hrtime.bigint();
+
+  await faceapi.tf.setBackend('tensorflow');
+  await faceapi.tf.enableProdMode();
+  await faceapi.tf.ENV.set('DEBUG', false);
+  await faceapi.tf.ready();
+
+  log.state(`Version: TensorFlow/JS ${faceapi.tf?.version_core} FaceAPI ${faceapi.version.faceapi} Backend: ${faceapi.tf?.getBackend()}`);
+
+  log.info('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 });
+
+  const dir = fs.readdirSync(imgPathRoot);
+  for (const img of dir) {
+    if (!img.toLocaleLowerCase().endsWith('.jpg')) continue;
+    const tensor = await image(path.join(imgPathRoot, img));
+    const result = await faceapi
+      .detectAllFaces(tensor, optionsSSDMobileNet)
+      .withFaceLandmarks()
+      .withFaceExpressions()
+      .withFaceDescriptors()
+      .withAgeAndGender();
+    log.data('Image:', img, 'Detected faces:', result.length);
+    tensor.dispose();
+  }
+  const t1 = process.hrtime.bigint();
+  log.info('Processed', dir.length, 'images in', Math.trunc(parseInt(t1 - t0) / 1000 / 1000), 'ms');
+}
+
+main();

example/node.js

@@ -1,84 +0,0 @@
-process.stderr.write = null; // silly hack to stock tfjs logging too much to stderr
-
-const fs = require('fs');
-const path = require('path');
-const tf = require('@tensorflow/tfjs-node');
-const faceapi = require('../dist/face-api.node.js');
-// if you have module installed, this would be 
-// 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();

package-lock.json

@@ -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==",
+      "integrity": "sha512-UaDAFoEJwPw8248u9WQjVexP24wMiglHMWWd4X0gwukZuDw+CkoLddVF8335OYa+pXbP+t/rwx+E50f5rd5IhQ=="
-      "dev": true
     },
     "abbrev": {
       "version": "1.1.1",

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": {}
 }