FaceMatcher

examples/public/js/bbt.js

@@ -30,40 +30,24 @@ function renderFaceImageSelectList(selectListId, onChange, initialValue) {
 }
 
 // fetch first image of each class and compute their descriptors
-async function initBbtFaceDescriptors(net, numImagesForTraining = 1) {
+async function createBbtFaceMatcher(numImagesForTraining = 1) {
   const maxAvailableImagesPerClass = 5
   numImagesForTraining = Math.min(numImagesForTraining, maxAvailableImagesPerClass)
-  return Promise.all(classes.map(
+
+  const labeledFaceDescriptors = await Promise.all(classes.map(
     async className => {
       const descriptors = []
       for (let i = 1; i < (numImagesForTraining + 1); i++) {
         const img = await faceapi.fetchImage(getFaceImageUri(className, i))
         descriptors.push(await faceapi.computeFaceDescriptor(img))
       }
-      return {
-        descriptors,
-        className
-      }
-    }
-  ))
-}
 
-function getBestMatch(descriptorsByClass, queryDescriptor) {
-  function computeMeanDistance(descriptorsOfClass) {
-    return faceapi.round(
-      descriptorsOfClass
-        .map(d => faceapi.euclideanDistance(d, queryDescriptor))
-        .reduce((d1, d2) => d1 + d2, 0)
-          / (descriptorsOfClass.length || 1)
+      return new faceapi.LabeledFaceDescriptors(
+        className,
+        descriptors
       )
-  }
-  return descriptorsByClass
-    .map(
-      ({ descriptors, className }) => ({
-        distance: computeMeanDistance(descriptors),
-        className
-      })
-    )
-    .reduce((best, curr) => best.distance < curr.distance ? best : curr)
-}
+    }
+  ))
 
+  return new faceapi.FaceMatcher(labeledFaceDescriptors)
+}
\ No newline at end of file

examples/views/batchFaceRecognition.html

@@ -48,8 +48,7 @@
 
   <script>
     let images = []
-    let referenceDescriptorsByClass = []
-    let descriptorsByFace = []
+    let faceMatcher = null
     let numImages = 16
     let maxDistance = 0.6
 
@@ -68,15 +67,12 @@
       const canvas = faceapi.createCanvasFromMedia(img)
       $('#faceContainer').append(canvas)
 
-      const bestMatch = getBestMatch(referenceDescriptorsByClass, descriptor)
-      const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})`
-
       const x = 20, y = canvas.height - 20
       faceapi.drawText(
         canvas.getContext('2d'),
         x,
         y,
-        text,
+        faceMatcher.findBestMatch(descriptor).toString(),
         Object.assign(faceapi.getDefaultDrawOptions(), { color: 'red', fontSize: 16 })
       )
     }
@@ -106,7 +102,7 @@
 
     async function run() {
       await faceapi.loadFaceRecognitionModel('/')
-      referenceDescriptorsByClass = await initBbtFaceDescriptors(faceapi.recognitionNet, 1)
+      faceMatcher = await createBbtFaceMatcher(1)
       $('#loader').hide()
 
       const imgUris = classes

examples/views/bbtFaceRecognition.html

@@ -63,13 +63,10 @@
   </div>
 
   <script>
-    // for 150 x 150 sized face images 0.6 is a good threshold to
-    // judge whether two face descriptors are similar or not
-    const threshold = 0.6
     let interval = 2000
 
     let isStop = false
-    let referenceDescriptorsByClass = []
+    let faceMatcher = null
     let currImageIdx = 2, currClassIdx = 0
     let to = null
 
@@ -116,8 +113,8 @@
         const descriptor = await faceapi.computeFaceDescriptor(input)
         displayTimeStats(Date.now() - ts)
 
-        const bestMatch = getBestMatch(referenceDescriptorsByClass, descriptor)
-        $('#prediction').val(`${bestMatch.distance < threshold ? bestMatch.className : 'unknown'} (${bestMatch.distance})`)
+        const bestMatch = faceMatcher.findBestMatch(descriptor)
+        $('#prediction').val(bestMatch.toString())
 
         currImageIdx = currClassIdx === (classes.length - 1)
           ? currImageIdx + 1
@@ -138,7 +135,7 @@
 
         setStatusText('computing initial descriptors...')
 
-        referenceDescriptorsByClass = await initBbtFaceDescriptors(faceapi.recognitionNet)
+        faceMatcher = await createBbtFaceMatcher(1)
         $('#loader').hide()
 
         runFaceRecognition()

examples/views/faceRecognition.html

@@ -139,8 +139,7 @@
   </body>
 
   <script>
-    const maxDescriptorDistance = 0.6
-    let referenceDescriptorsByClass = []
+    let faceMatcher = null
 
     async function updateResults() {
       if (!isFaceDetectionModelLoaded()) {
@@ -159,20 +158,17 @@
     }
 
     function drawFaceRecognitionResults(results) {
-      const { width, height } = $('#inputImg').get(0)
       const canvas = $('#overlay').get(0)
-      canvas.width = width
-      canvas.height = height
-
       // resize detection and landmarks in case displayed image is smaller than
       // original size
-      results = results.map(res => res.forSize(width, height))
-
-      const boxesWithText = results.map(({ detection, descriptor }) => {
-        const bestMatch = getBestMatch(referenceDescriptorsByClass, descriptor)
-        const text = `${bestMatch.distance < maxDescriptorDistance ? bestMatch.className : 'unknown'} (${bestMatch.distance})`
-        return new faceapi.BoxWithText(detection.box, text)
-      })
+      resizedResults = resizeCanvasAndResults($('#inputImg').get(0), canvas, results)
+
+      const boxesWithText = resizedResults.map(({ detection, descriptor }) =>
+        new faceapi.BoxWithText(
+          detection.box,
+          faceMatcher.findBestMatch(descriptor).toString()
+        )
+      )
       faceapi.drawDetection(canvas, boxesWithText)
     }
 
@@ -182,8 +178,8 @@
       await faceapi.loadFaceLandmarkModel('/')
       await faceapi.loadFaceRecognitionModel('/')
 
-      // initialize reference descriptors (1 per bbt character)
-      referenceDescriptorsByClass = await initBbtFaceDescriptors(faceapi.recognitionNet, 1)
+      // initialize face matcher with 1 reference descriptor per bbt character
+      faceMatcher = await createBbtFaceMatcher(1)
 
       // start processing image
       updateResults()

src/classes/FaceMatch.ts

+import { round } from 'tfjs-image-recognition-base';
+
+export class FaceMatch {
+  private _label: string
+  private _distance: number
+
+  constructor(label: string, distance: number) {
+    this._label = label
+    this._distance = distance
+  }
+
+  public get label(): string { return this._label }
+  public get distance(): number { return this._distance }
+
+  public toString(withDistance: boolean = true): string {
+    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`
+  }
+}
\ No newline at end of file

src/classes/FullFaceDescription.ts

@@ -4,7 +4,7 @@ import { FaceLandmarks } from './FaceLandmarks';
 import { FaceLandmarks68 } from './FaceLandmarks68';
 
 export interface IFullFaceDescription<TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>
-  extends IFaceDetectionWithLandmarks<TFaceLandmarks>{
+  extends IFaceDetectionWithLandmarks<TFaceLandmarks> {
 
   detection: FaceDetection,
   landmarks: TFaceLandmarks,

src/classes/LabeledFaceDescriptors.ts

+export class LabeledFaceDescriptors {
+  private _label: string
+  private _descriptors: Float32Array[]
+
+  constructor(label: string, descriptors: Float32Array[]) {
+    if (!(typeof label === 'string')) {
+      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')
+    }
+
+    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {
+      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')
+    }
+
+    this._label = label
+    this._descriptors = descriptors
+  }
+
+  public get label(): string { return this._label }
+  public get descriptors(): Float32Array[] { return this._descriptors }
+}
\ No newline at end of file

src/classes/index.ts

@@ -2,4 +2,6 @@ export * from './FaceDetection';
 export * from './FaceLandmarks';
 export * from './FaceLandmarks5';
 export * from './FaceLandmarks68';
-export * from './FullFaceDescription';
+export * from './FaceMatch';
\ No newline at end of file
+export * from './FullFaceDescription';
+export * from './LabeledFaceDescriptors';
\ No newline at end of file

src/faceLandmarkNet/FaceLandmark68Net.ts

@@ -38,7 +38,7 @@ function denseBlock(
 export class FaceLandmark68Net extends FaceLandmark68NetBase<NetParams> {
 
   constructor() {
-    super('FaceLandmark68LargeNet')
+    super('FaceLandmark68Net')
   }
 
   public runNet(input: NetInput): tf.Tensor2D {
@@ -46,7 +46,7 @@ export class FaceLandmark68Net extends FaceLandmark68NetBase<NetParams> {
     const { params } = this
 
     if (!params) {
-      throw new Error('FaceLandmark68LargeNet - load model before inference')
+      throw new Error('FaceLandmark68Net - load model before inference')
     }
 
     return tf.tidy(() => {

src/globalApi/FaceMatcher.ts

+import { FaceMatch } from '../classes/FaceMatch';
+import { FullFaceDescription } from '../classes/FullFaceDescription';
+import { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';
+import { euclideanDistance } from '../euclideanDistance';
+
+export class FaceMatcher {
+
+  private _labeledDescriptors: LabeledFaceDescriptors[]
+  private _distanceThreshold: number
+
+  constructor(
+    inputs: LabeledFaceDescriptors | FullFaceDescription | Float32Array | Array<LabeledFaceDescriptors | FullFaceDescription | Float32Array>,
+    distanceThreshold: number = 0.6
+  ) {
+
+    this._distanceThreshold = distanceThreshold
+
+    const inputArray = Array.isArray(inputs) ? inputs : [inputs]
+
+    if (!inputArray.length) {
+      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)
+    }
+
+    let count = 1
+    const createUniqueLabel = () => `person ${count++}`
+
+    this._labeledDescriptors = inputArray.map((desc) => {
+      if (desc instanceof LabeledFaceDescriptors) {
+        return desc
+      }
+
+      if (desc instanceof FullFaceDescription) {
+        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])
+      }
+
+      if (desc instanceof Float32Array) {
+        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])
+      }
+
+      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | FullFaceDescription | Float32Array | Array<LabeledFaceDescriptors | FullFaceDescription | Float32Array>`)
+    })
+  }
+
+  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }
+  public get distanceThreshold(): number { return this._distanceThreshold }
+
+  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {
+    return descriptors
+      .map(d => euclideanDistance(d, queryDescriptor))
+      .reduce((d1, d2) => d1 + d2, 0)
+        / (descriptors.length || 1)
+  }
+
+  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {
+    return this.labeledDescriptors
+      .map(({ descriptors, label }) => new FaceMatch(
+          label,
+          this.computeMeanDistance(queryDescriptor, descriptors)
+      ))
+      .reduce((best, curr) => best.distance < curr.distance ? best : curr)
+  }
+
+  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {
+    const bestMatch = this.matchDescriptor(queryDescriptor)
+    return bestMatch.distance < this.distanceThreshold
+      ? bestMatch
+      : new FaceMatch('unknown', bestMatch.distance)
+  }
+
+}
\ No newline at end of file

src/globalApi/index.ts

@@ -3,6 +3,7 @@ export * from './ComposableTask'
 export * from './ComputeFaceDescriptorsTasks'
 export * from './DetectFacesTasks'
 export * from './DetectFaceLandmarksTasks'
+export * from './FaceMatcher'
 export * from './nets'
 export * from './types'