fixed the face alignment bug in allFaces + default parameters

src/FaceLandmarks.ts

@@ -105,7 +105,7 @@ export class FaceLandmarks {
     const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))
     const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))
 
-    return new Rect(x, y, Math.min(size, this._imageWidth - x), Math.min(size, this._imageHeight - y))
+    return new Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y))
   }
 
   protected getRefPointsForAlignment(): Point[] {

src/allFacesFactory.ts

@@ -2,25 +2,45 @@ import { extractFaceTensors } from './extractFaceTensors';
 import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
 import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
 import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
+import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
 import { FullFaceDescription } from './FullFaceDescription';
 import { Mtcnn } from './mtcnn/Mtcnn';
 import { MtcnnForwardParams } from './mtcnn/types';
 import { Rect } from './Rect';
 import { TNetInput } from './types';
 
+function computeDescriptorsFactory(
+  recognitionNet: FaceRecognitionNet
+) {
+  return async function(input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) {
+    const alignedFaceTensors = await extractFaceTensors(input, alignedFaceBoxes)
+
+    const descriptors = useBatchProcessing
+      ? await recognitionNet.computeFaceDescriptor(alignedFaceTensors) as Float32Array[]
+      : await Promise.all(alignedFaceTensors.map(
+        faceTensor => recognitionNet.computeFaceDescriptor(faceTensor)
+      )) as Float32Array[]
+
+    alignedFaceTensors.forEach(t => t.dispose())
+
+    return descriptors
+  }
+}
+
 export function allFacesFactory(
   detectionNet: FaceDetectionNet,
   landmarkNet: FaceLandmarkNet,
-  computeDescriptors: (input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) => Promise<Float32Array[]>
+  recognitionNet: FaceRecognitionNet
 ) {
+  const computeDescriptors = computeDescriptorsFactory(recognitionNet)
+
   return async function(
     input: TNetInput,
-    minConfidence: number,
+    minConfidence: number = 0.8,
     useBatchProcessing: boolean = false
   ): Promise<FullFaceDescription[]> {
 
     const detections = await detectionNet.locateFaces(input, minConfidence)
-
     const faceTensors = await extractFaceTensors(input, detections)
 
     const faceLandmarksByFace = useBatchProcessing
@@ -50,11 +70,13 @@ export function allFacesFactory(
 
 export function allFacesMtcnnFactory(
   mtcnn: Mtcnn,
-  computeDescriptors: (input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) => Promise<Float32Array[]>
+  recognitionNet: FaceRecognitionNet
 ) {
+  const computeDescriptors = computeDescriptorsFactory(recognitionNet)
+
   return async function(
     input: TNetInput,
-    mtcnnForwardParams: MtcnnForwardParams,
+    mtcnnForwardParams: MtcnnForwardParams = {},
     useBatchProcessing: boolean = false
   ): Promise<FullFaceDescription[]> {
 

src/faceDetectionNet/FaceDetectionNet.ts

@@ -50,7 +50,7 @@ export class FaceDetectionNet extends NeuralNetwork<NetParams> {
   public async locateFaces(
     input: TNetInput,
     minConfidence: number = 0.8,
-    maxResults: number = 100,
+    maxResults: number = 100
   ): Promise<FaceDetection[]> {
 
     const netInput = await toNetInput(input, true)

src/globalApi.ts

 import * as tf from '@tensorflow/tfjs-core';
 
 import { allFacesFactory, allFacesMtcnnFactory } from './allFacesFactory';
-import { extractFaceTensors } from './extractFaceTensors';
 import { FaceDetection } from './FaceDetection';
 import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
 import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
@@ -11,7 +10,6 @@ import { FullFaceDescription } from './FullFaceDescription';
 import { Mtcnn } from './mtcnn/Mtcnn';
 import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
 import { NetInput } from './NetInput';
-import { Rect } from './Rect';
 import { TNetInput } from './types';
 
 export const detectionNet = new FaceDetectionNet()
@@ -79,39 +77,25 @@ export function mtcnn(
   return nets.mtcnn.forward(input, forwardParams)
 }
 
-export const allFaces: (
+export type allFacesFunction = (
   input: tf.Tensor | NetInput | TNetInput,
-  minConfidence: number,
+  minConfidence?: number,
   useBatchProcessing?: boolean
-) => Promise<FullFaceDescription[]> = allFacesFactory(
-  detectionNet,
-  landmarkNet,
-  computeDescriptorsFactory(nets.faceRecognitionNet)
+) => Promise<FullFaceDescription[]>
+
+export const allFaces: allFacesFunction = allFacesFactory(
+  nets.ssdMobilenet,
+  nets.faceLandmark68Net,
+  nets.faceRecognitionNet
 )
 
-export const allFacesMtcnn: (
+export type allFacesMtcnnFunction = (
   input: tf.Tensor | NetInput | TNetInput,
-  mtcnnForwardParams: MtcnnForwardParams,
+  mtcnnForwardParams?: MtcnnForwardParams,
   useBatchProcessing?: boolean
-) => Promise<FullFaceDescription[]> = allFacesMtcnnFactory(
-  nets.mtcnn,
-  computeDescriptorsFactory(nets.faceRecognitionNet)
-)
+) => Promise<FullFaceDescription[]>
 
-function computeDescriptorsFactory(
-  recognitionNet: FaceRecognitionNet
-) {
-  return async function(input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) {
-    const alignedFaceTensors = await extractFaceTensors(input, alignedFaceBoxes)
-
-    const descriptors = useBatchProcessing
-      ? await recognitionNet.computeFaceDescriptor(alignedFaceTensors) as Float32Array[]
-      : await Promise.all(alignedFaceTensors.map(
-        faceTensor => recognitionNet.computeFaceDescriptor(faceTensor)
-      )) as Float32Array[]
-
-    alignedFaceTensors.forEach(t => t.dispose())
-
-    return descriptors
-  }
-}
\ No newline at end of file
+export const allFacesMtcnn: allFacesMtcnnFunction = allFacesMtcnnFactory(
+  nets.mtcnn,
+  nets.faceRecognitionNet
+)
\ No newline at end of file

src/mtcnn/Mtcnn.ts

@@ -28,7 +28,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
 
   public async forwardInput(
     input: NetInput,
-    forwardParams: MtcnnForwardParams
+    forwardParams: MtcnnForwardParams = {}
   ): Promise<{ results: MtcnnResult[], stats: any }> {
 
     const { params } = this
@@ -132,7 +132,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
 
   public async forward(
     input: TNetInput,
-    forwardParams: MtcnnForwardParams
+    forwardParams: MtcnnForwardParams = {}
   ): Promise<MtcnnResult[]> {
     return (
       await this.forwardInput(
@@ -144,7 +144,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
 
   public async forwardWithStats(
     input: TNetInput,
-    forwardParams: MtcnnForwardParams
+    forwardParams: MtcnnForwardParams = {}
   ): Promise<{ results: MtcnnResult[], stats: any }> {
     return this.forwardInput(
       await toNetInput(input, true, true),