improve face compare performance

CHANGELOG.md

@@ -9,7 +9,7 @@
   
 ## Changelog
   
-### **HEAD -> master** 2022/08/24 mandic00@live.com
+### **HEAD -> master** 2022/09/04 mandic00@live.com
 
 
 ### **1.7.3** 2022/08/24 mandic00@live.com

demo/node-face-compare.js

@@ -0,0 +1,35 @@
+/**
+ * FaceAPI demo that loads two images and finds similarity most prominant face in each image
+ */
+
+const fs = require('fs');
+const tf = require('@tensorflow/tfjs-node');
+const faceapi = require('../dist/face-api.node');
+
+let optionsSSDMobileNet;
+
+const getDescriptors = async (imageFile) => {
+  const buffer = fs.readFileSync(imageFile);
+  const tensor = tf.node.decodeImage(buffer, 3);
+  const faces = await faceapi.detectAllFaces(tensor, optionsSSDMobileNet)
+    .withFaceLandmarks()
+    .withFaceDescriptors();
+  tf.dispose(tensor);
+  return faces.map((face) => face.descriptor);
+};
+
+const main = async (file1, file2) => {
+  console.log('input images:', file1, file2); // eslint-disable-line no-console
+  await tf.ready();
+  await faceapi.nets.ssdMobilenetv1.loadFromDisk('model');
+  optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence: 0.5, maxResults: 1 });
+  await faceapi.nets.faceLandmark68Net.loadFromDisk('model');
+  await faceapi.nets.faceRecognitionNet.loadFromDisk('model');
+  const desc1 = await getDescriptors(file1);
+  const desc2 = await getDescriptors(file2);
+  const distance = faceapi.euclideanDistance(desc1[0], desc2[0]); // only compare first found face in each image
+  console.log('distance between most prominant detected faces:', distance); // eslint-disable-line no-console
+  console.log('similarity between most prominant detected faces:', 1 - distance); // eslint-disable-line no-console
+};
+
+main('demo/sample1.jpg', 'demo/sample2.jpg');

package.json

@@ -42,7 +42,7 @@
     "tfjs"
   ],
   "devDependencies": {
-    "@microsoft/api-extractor": "^7.30.0",
+    "@microsoft/api-extractor": "^7.31.0",
     "@tensorflow/tfjs": "^3.20.0",
     "@tensorflow/tfjs-backend-cpu": "^3.20.0",
     "@tensorflow/tfjs-backend-wasm": "^3.20.0",
@@ -54,15 +54,15 @@
     "@tensorflow/tfjs-layers": "^3.20.0",
     "@tensorflow/tfjs-node": "^3.20.0",
     "@tensorflow/tfjs-node-gpu": "^3.20.0",
-    "@types/node": "^18.7.14",
+    "@types/node": "^18.7.18",
     "@types/offscreencanvas": "^2019.7.0",
-    "@typescript-eslint/eslint-plugin": "^5.36.1",
-    "@typescript-eslint/parser": "^5.36.1",
-    "@vladmandic/build": "^0.7.11",
+    "@typescript-eslint/eslint-plugin": "^5.37.0",
+    "@typescript-eslint/parser": "^5.37.0",
+    "@vladmandic/build": "^0.7.12",
     "@vladmandic/pilogger": "^0.4.6",
     "@vladmandic/tfjs": "github:vladmandic/tfjs",
     "esbuild": "^0.15.7",
-    "eslint": "^8.23.0",
+    "eslint": "^8.23.1",
     "eslint-config-airbnb-base": "^15.0.0",
     "eslint-plugin-import": "^2.26.0",
     "eslint-plugin-json": "^3.1.0",
@@ -72,6 +72,6 @@
     "seedrandom": "^3.0.5",
     "tslib": "^2.4.0",
     "typedoc": "^0.23.14",
-    "typescript": "4.8.2"
+    "typescript": "4.8.3"
   }
 }

src/NeuralNetwork.ts

@@ -103,7 +103,7 @@ export abstract class NeuralNetwork<TNetParams> {
     const { readFile } = env.getEnv();
     const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());
     const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));
-    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);
+    const loadWeights = tf['io'].weightsLoaderFactory(fetchWeightsFromDisk);
     const manifest = JSON.parse((await readFile(manifestUri)).toString());
     const weightMap = await loadWeights(manifest, modelBaseUri);
     this.loadFromWeightMap(weightMap);

src/dom/NetInput.ts

@@ -131,14 +131,14 @@ export class NetInput {
           imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);
 
           if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
-            imgTensor = tf.image.resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);
+            imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);
           }
 
           return imgTensor.as3D(inputSize, inputSize, 3);
         }
 
         if (input instanceof env.getEnv().Canvas) {
-          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
+          return tf['browser'].fromPixels(imageToSquare(input, inputSize, isCenterInputs));
         }
 
         throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);

src/dom/imageTensorToCanvas.ts

@@ -11,7 +11,7 @@ export async function imageTensorToCanvas(
 
   const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
   const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
-  await tf.browser.toPixels(imgTensor3D, targetCanvas);
+  await tf['browser'].toPixels(imgTensor3D, targetCanvas);
 
   imgTensor3D.dispose();
 

src/dom/loadWeightMap.ts

@@ -8,7 +8,8 @@ export async function loadWeightMap(
   defaultModelName: string,
 ): Promise<tf.NamedTensorMap> {
   const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);
+  // @ts-ignore
   const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);
   // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];
-  return tf.io.loadWeights(manifest, modelBaseUri);
+  return tf['io'].loadWeights(manifest, modelBaseUri);
 }

src/euclideanDistance.ts

@@ -1,12 +1,10 @@
 export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {
   if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');
-
   const desc1 = Array.from(arr1);
   const desc2 = Array.from(arr2);
-
   return Math.sqrt(
     desc1
       .map((val, i) => val - desc2[i])
-      .reduce((res, diff) => res + (diff ** 2), 0),
+      .reduce((res, diff) => res + (diff * diff), 0),
   );
 }