refactor FaceDetection result

examples/views/detectAndRecognizeFaces.html

@@ -108,7 +108,7 @@
       descriptors.forEach((descriptor, i) => {
         const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor)
         const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})`
-        const { x, y, height: boxHeight } = detectionsForSize[i].box
+        const { x, y, height: boxHeight } = detectionsForSize[i].getBox()
         faceapi.drawText(
           canvas.getContext('2d'),
           x,

src/Rect.ts

+export class Rect {
+  public x: number
+  public y: number
+  public width: number
+  public height: number
+
+  constructor(x: number, y: number, width: number, height: number) {
+    this.x = x
+    this.y = y
+    this.width = width
+    this.height = height
+  }
+}
\ No newline at end of file

src/extractFaceTensors.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
-import { NetInput } from './NetInput';
+import { FaceDetection } from './faceDetectionNet/FaceDetection';
 import { getImageTensor } from './getImageTensor';
+import { NetInput } from './NetInput';
 import { TNetInput } from './types';
 
 /**
@@ -18,7 +18,7 @@ import { TNetInput } from './types';
  */
 export function extractFaceTensors(
   image: tf.Tensor | NetInput | TNetInput,
-  detections: FaceDetectionResult[]
+  detections: FaceDetection[]
 ): tf.Tensor4D[] {
   return tf.tidy(() => {
     const imgTensor = getImageTensor(image)
@@ -27,7 +27,7 @@ export function extractFaceTensors(
     const [batchSize, imgHeight, imgWidth, numChannels] = imgTensor.shape
 
     const faceTensors = detections.map(det => {
-      const { x, y, width, height } = det.forSize(imgWidth, imgHeight).box
+      const { x, y, width, height } = det.forSize(imgWidth, imgHeight).getBox()
       return tf.slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels])
     })
 

src/extractFaces.ts

-import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
+import { FaceDetection } from './faceDetectionNet/FaceDetection';
 import { createCanvas, getContext2dOrThrow } from './utils';
 
 /**
@@ -10,12 +10,12 @@ import { createCanvas, getContext2dOrThrow } from './utils';
  */
 export function extractFaces(
   image: HTMLCanvasElement,
-  detections: FaceDetectionResult[]
+  detections: FaceDetection[]
 ): HTMLCanvasElement[] {
   const ctx = getContext2dOrThrow(image)
 
   return detections.map(det => {
-    const { x, y, width, height } = det.forSize(image.width, image.height).box
+    const { x, y, width, height } = det.forSize(image.width, image.height).getBox()
 
     const faceImg = createCanvas({ width, height })
     getContext2dOrThrow(faceImg)

src/faceDetectionNet/FaceDetection.ts

+import { Rect } from '../Rect';
+import { Dimensions } from '../types';
+
+export class FaceDetection {
+  private _score: number
+  private _box: Rect
+  private _imageWidth: number
+  private _imageHeight: number
+
+  constructor(
+    score: number,
+    relativeBox: Rect,
+    imageDims: Dimensions
+  ) {
+    const { width, height } = imageDims
+    this._imageWidth = width
+    this._imageHeight = height
+    this._score = score
+    this._box = new Rect(
+      Math.floor(relativeBox.x * width),
+      Math.floor(relativeBox.y * height),
+      Math.floor(relativeBox.width * width),
+      Math.floor(relativeBox.height * height)
+    )
+  }
+
+  public getScore() {
+    return this._score
+  }
+
+  public getBox() {
+    return this._box
+  }
+
+  public getRelativeBox() {
+    return new Rect(
+      this._box.x / this._imageWidth,
+      this._box.y / this._imageHeight,
+      this._box.width / this._imageWidth,
+      this._box.height / this._imageHeight
+    )
+  }
+
+  public forSize(width: number, height: number): FaceDetection {
+    return new FaceDetection(
+      this._score,
+      this.getRelativeBox(),
+      { width, height}
+    )
+  }
+}
\ No newline at end of file

src/faceDetectionNet/FaceDetectionResult.ts

-import { FaceDetectionNet } from './types';
-
-export class FaceDetectionResult {
-  private _score: number
-  private _topRelative: number
-  private _leftRelative: number
-  private _bottomRelative: number
-  private _rightRelative: number
-
-  constructor(
-    score: number,
-    topRelative: number,
-    leftRelative: number,
-    bottomRelative: number,
-    rightRelative: number
-  ) {
-    this._score = score
-    this._topRelative = Math.max(0, topRelative),
-    this._leftRelative = Math.max(0, leftRelative),
-    this._bottomRelative  = Math.min(1.0, bottomRelative),
-    this._rightRelative = Math.min(1.0, rightRelative)
-  }
-
-  public forSize(width: number, height: number): FaceDetectionNet.Detection {
-    const x = Math.floor(this._leftRelative * width)
-    const y = Math.floor(this._topRelative * height)
-    return {
-      score: this._score,
-      box: {
-        x,
-        y,
-        width: Math.floor(this._rightRelative * width) - x,
-        height: Math.floor(this._bottomRelative * height) - y
-      }
-    }
-  }
-}
\ No newline at end of file

src/faceDetectionNet/index.ts

@@ -3,14 +3,15 @@ import * as tf from '@tensorflow/tfjs-core';
 import { getImageTensor } from '../getImageTensor';
 import { NetInput } from '../NetInput';
 import { padToSquare } from '../padToSquare';
-import { TNetInput } from '../types';
+import { TNetInput, Dimensions } from '../types';
 import { extractParams } from './extractParams';
-import { FaceDetectionResult } from './FaceDetectionResult';
+import { FaceDetection } from './FaceDetection';
 import { mobileNetV1 } from './mobileNetV1';
 import { nonMaxSuppression } from './nonMaxSuppression';
 import { outputLayer } from './outputLayer';
 import { predictionLayer } from './predictionLayer';
 import { resizeLayer } from './resizeLayer';
+import { Rect } from '../Rect';
 
 export function faceDetectionNet(weights: Float32Array) {
   const params = extractParams(weights)
@@ -40,9 +41,10 @@ export function faceDetectionNet(weights: Float32Array) {
     input: tf.Tensor | NetInput,
     minConfidence: number = 0.8,
     maxResults: number = 100,
-  ): Promise<FaceDetectionResult[]> {
+  ): Promise<FaceDetection[]> {
 
     let paddedHeightRelative = 1, paddedWidthRelative = 1
+    let imageDimensions: Dimensions | undefined
 
     const {
       boxes: _boxes,
@@ -51,6 +53,7 @@ export function faceDetectionNet(weights: Float32Array) {
 
       let imgTensor = getImageTensor(input)
       const [height, width] = imgTensor.shape.slice(1)
+      imageDimensions = { width, height }
 
       imgTensor = padToSquare(imgTensor)
       paddedHeightRelative = imgTensor.shape[1] / height
@@ -80,13 +83,26 @@ export function faceDetectionNet(weights: Float32Array) {
     )
 
     const results = indices
-      .map(idx => new FaceDetectionResult(
+      .map(idx => {
+        const [top, bottom] = [
+          Math.max(0, boxes.get(idx, 0)),
+          Math.min(1.0, boxes.get(idx, 2))
+        ].map(val => val * paddedHeightRelative)
+        const [left, right] = [
+          Math.max(0, boxes.get(idx, 1)),
+          Math.min(1.0, boxes.get(idx, 3))
+        ].map(val => val * paddedWidthRelative)
+        return new FaceDetection(
           scoresData[idx],
-        boxes.get(idx, 0) * paddedHeightRelative,
-        boxes.get(idx, 1) * paddedWidthRelative,
-        boxes.get(idx, 2) * paddedHeightRelative,
-        boxes.get(idx, 3) * paddedWidthRelative
-      ))
+          new Rect(
+            left,
+            top,
+            right - left,
+            bottom - top
+          ),
+          imageDimensions as Dimensions
+        )
+      })
 
     boxes.dispose()
     scores.dispose()

src/faceDetectionNet/types.ts

@@ -62,15 +62,4 @@ export namespace FaceDetectionNet {
     prediction_layer_params: PredictionLayerParams,
     output_layer_params: OutputLayerParams
   }
-
-  export type Detection = {
-    score: number
-    box: {
-      x: number,
-      y: number,
-      width: number,
-      height: number
-    }
-  }
-
 }

src/utils.ts

-import { FaceDetectionNet } from './faceDetectionNet/types';
+import { FaceDetection } from './faceDetectionNet/FaceDetection';
 import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
 import { Dimensions, DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
 
@@ -115,7 +115,7 @@ export function drawText(
 
 export function drawDetection(
   canvasArg: string | HTMLCanvasElement,
-  detection: FaceDetectionNet.Detection | FaceDetectionNet.Detection[],
+  detection: FaceDetection | FaceDetection[],
   options?: DrawBoxOptions & DrawTextOptions & { withScore: boolean }
 ) {
   const canvas = getElement(canvasArg)
@@ -129,16 +129,11 @@ export function drawDetection(
 
   detectionArray.forEach((det) => {
     const {
-      score,
-      box
-    } = det
-
-    const {
       x,
       y,
       width,
       height
-    } = box
+    } = det.getBox()
 
     const drawOptions = Object.assign(
       getDefaultDrawOptions(),
@@ -161,7 +156,7 @@ export function drawDetection(
         ctx,
         x,
         y,
-        `${round(score)}`,
+        `${round(det.getScore())}`,
         drawOptions
       )
     }