add node-canvas demo

README.md

@@ -69,10 +69,17 @@ Example can be accessed directly using Git pages using URL:
 
 ### NodeJS
 
-Two NodeJS examples are:
+Three NodeJS examples are:
 
 - `/demo/node-singleprocess.js`:  
   Regular usage of `FaceAPI` from `NodeJS`  
+- `/demo/node-singleprocess.js`:  
+  Regular usage of `FaceAPI` from `NodeJS`  
+  Using `TFJS` native methods to load images
+- `/demo/node-canvas.js`:  
+  Regular usage of `FaceAPI` from `NodeJS`  
+  Using external `canvas` module to load images  
+  Which also allows for image drawing and saving inside `NodeJS`
 - `/demo/node-multiprocess.js`:  
   Multiprocessing showcase that uses pool of worker processes  
   (`node-multiprocess-worker.js`)  

demo/node-canvas.js

@@ -0,0 +1,87 @@
+// @ts-nocheck
+
+const fs = require('fs');
+const process = require('process');
+const path = require('path');
+// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
+const log = require('@vladmandic/pilogger');
+// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
+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 = './demo'; // modify to include your sample images
+const minScore = 0.1;
+const maxResults = 5;
+let optionsSSDMobileNet;
+
+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 detect(tensor) {
+  const result = await faceapi
+    .detectAllFaces(tensor, optionsSSDMobileNet)
+    .withFaceLandmarks()
+    .withFaceExpressions()
+    .withFaceDescriptors()
+    .withAgeAndGender();
+  return result;
+}
+
+async function main() {
+  log.header();
+  log.info('FaceAPI single-process test');
+
+  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);
+  optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: minScore, maxResults });
+
+  if (process.argv.length !== 3) {
+    const t0 = process.hrtime.bigint();
+    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 detect(tensor);
+      log.data('Image:', img, 'Detected faces:', result.length);
+      for (const i of result) {
+        log.data('Gender:', Math.round(100 * i.genderProbability), 'probability', i.gender, 'with age', Math.round(10 * i.age) / 10);
+      }
+      tensor.dispose();
+    }
+    const t1 = process.hrtime.bigint();
+    log.info('Processed', dir.length, 'images in', Math.trunc(parseInt(t1 - t0) / 1000 / 1000), 'ms');
+  } else {
+    const param = process.argv[2];
+    if (fs.existsSync(param)) {
+      const tensor = await image(param);
+      const result = await detect(tensor);
+      log.data('Image:', param, 'Detected faces:', result.length);
+      for (const i of result) {
+        log.data('Gender:', Math.round(100 * i.genderProbability), 'probability', i.gender, 'with age', Math.round(10 * i.age) / 10);
+      }
+      tensor.dispose();
+    }
+  }
+}
+
+main();

demo/node-singleprocess.js

@@ -5,8 +5,10 @@ const process = require('process');
 const path = require('path');
 // eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
 const log = require('@vladmandic/pilogger');
-// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
+// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require, no-unused-vars
 const tf = require('@tensorflow/tfjs-node');
+// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
+const canvas = require('canvas');
 const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'
 
 const modelPathRoot = '../model';
@@ -15,14 +17,12 @@ const minScore = 0.1;
 const maxResults = 5;
 let optionsSSDMobileNet;
 
-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 image(input) {
+  const img = canvas.loadImage(input);
+  const c = canvas.createCanvas(img.width, img.height);
+  const ctx = c.getContext('2d');
+  ctx.drawImage(img, 0, 0, img.width, img.height);
+  return c;
 }
 
 async function detect(tensor) {
@@ -39,6 +39,8 @@ async function main() {
   log.header();
   log.info('FaceAPI single-process test');
 
+  faceapi.env.monkeyPatch({ Canvas: canvas.Canvas, Image: canvas.Image, ImageData: canvas.ImageData });
+
   await faceapi.tf.setBackend('tensorflow');
   await faceapi.tf.enableProdMode();
   await faceapi.tf.ENV.set('DEBUG', false);
@@ -63,6 +65,9 @@ async function main() {
       const tensor = await image(path.join(imgPathRoot, img));
       const result = await detect(tensor);
       log.data('Image:', img, 'Detected faces:', result.length);
+      for (const i of result) {
+        log.data('Gender:', Math.round(100 * i.genderProbability), 'probability', i.gender, 'with age', Math.round(10 * i.age) / 10);
+      }
       tensor.dispose();
     }
     const t1 = process.hrtime.bigint();
@@ -74,7 +79,7 @@ async function main() {
       const result = await detect(tensor);
       log.data('Image:', param, 'Detected faces:', result.length);
       for (const i of result) {
-        log.data('Gender:', i.genderProbability, i.gender, 'Age:', i.age);
+        log.data('Gender:', Math.round(100 * i.genderProbability), 'probability', i.gender, 'with age', Math.round(10 * i.age) / 10);
       }
       tensor.dispose();
     }

package.json

@@ -44,12 +44,13 @@
     "@tensorflow/tfjs-node": "^3.3.0",
     "@tensorflow/tfjs-node-gpu": "^3.3.0",
     "@types/node": "^14.14.35",
-    "@typescript-eslint/eslint-plugin": "^4.18.0",
-    "@typescript-eslint/parser": "^4.18.0",
+    "@typescript-eslint/eslint-plugin": "^4.19.0",
+    "@typescript-eslint/parser": "^4.19.0",
     "@vladmandic/pilogger": "^0.2.15",
+    "canvas": "^2.7.0",
     "chokidar": "^3.5.1",
     "dayjs": "^1.10.4",
-    "esbuild": "^0.9.5",
+    "esbuild": "^0.9.6",
     "eslint": "^7.22.0",
     "eslint-config-airbnb-base": "^14.2.1",
     "eslint-plugin-import": "^2.22.1",
@@ -60,7 +61,7 @@
     "seedrandom": "^3.0.5",
     "simple-git": "^2.37.0",
     "tslib": "^2.1.0",
-    "typedoc": "^0.20.32",
+    "typedoc": "^0.20.33",
     "typescript": "^4.2.3"
   }
 }

src/ssdMobilenetv1/pointwiseConvLayer.ts

@@ -7,11 +7,11 @@ export function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams,
     let out = tf.conv2d(x, params.filters, strides, 'same');
     /*
     if (x.shape[1] === 512 && x.shape[3] === 3) {
-      console.log('Input:', x.shape, x.size); // input does not change (checked values)
-      console.log('Filter:', params.filters.shape, params.filters.size); // params do not change (checked values)
+      console.log('Input:', x.shape, x.size, 'sum:', x.reshape([786432]).sum().dataSync()[0]); // input does not change (checked values)
+      console.log('Filter:', params.filters.shape, params.filters.size, 'sum:', params.filters.reshape([864]).sum().dataSync()[0]); // params do not change (checked values)
       console.log('Strides', strides);
-      console.log('Conv2d Output:', out.shape, out.size, out.dataSync()[0]); // output has different values!
-      console.log('Sum of all Conv2D values:', tf.reshape(out, [2097152]).sum().dataSync()[0]); // silly sum just to see how much results diverged
+      console.log('Conv2d 1st 5 values:', out.shape, out.size, out.dataSync().slice(0, 5)); // output has different values!
+      console.log('Conv2D sum of all values:', tf.reshape(out, [2097152]).sum().dataSync()[0]); // silly sum just to see how much results diverged
     }
     */
     out = tf.add(out, params.batch_norm_offset);