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testcases for tinyYolov2
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‎test/tests/e2e/expectedResults.ts

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@@ -20,6 +20,15 @@ export const expectedMtcnnBoxes = [
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{ x: 451, y: 176, width: 122, height: 122 }
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]
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export const expectedTinyYolov2Boxes = [
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{ x: 52, y: 263, width: 106, height: 102 },
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{ x: 455, y: 191, width: 103, height: 97 },
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{ x: 236, y: 57, width: 90, height: 85 },
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{ x: 257, y: 243, width: 86, height: 95 },
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{ x: 578, y: 76, width: 86, height: 91 },
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{ x: 87, y: 30, width: 92, height: 93 }
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]
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export const expectedMtcnnFaceLandmarks = [
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[new Point(117, 58), new Point(156, 63), new Point(141, 86), new Point(109, 98), new Point(147, 104)],
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[new Point(82, 292), new Point(134, 304), new Point(104, 330), new Point(72, 342), new Point(120, 353)],

‎test/tests/e2e/tinyYolov2.test.ts

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@@ -0,0 +1,137 @@
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import * as faceapi from '../../../src';
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import { SizeType } from '../../../src/tinyYolov2/types';
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import { describeWithNets, expectAllTensorsReleased, expectRectClose } from '../../utils';
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import { expectedTinyYolov2Boxes } from './expectedResults';
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describe('tinyYolov2', () => {
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let imgEl: HTMLImageElement
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beforeAll(async () => {
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const img = await (await fetch('base/test/images/faces.jpg')).blob()
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imgEl = await faceapi.bufferToImage(img)
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})
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describeWithNets('quantized weights', { withTinyYolov2: { quantized: true } }, ({ tinyYolov2 }) => {
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it('inputSize lg, finds all faces', async () => {
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const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: SizeType.LG })
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const expectedScores = [0.86, 0.86, 0.85, 0.83, 0.81, 0.81]
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const maxBoxDelta = 3
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const boxOrder = [0, 1, 2, 3, 4, 5]
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expect(detections.length).toEqual(6)
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detections.forEach((det, i) => {
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expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
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expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
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})
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})
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it('inputSize md, finds all faces', async () => {
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const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: SizeType.MD })
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const expectedScores = [0.89, 0.87, 0.83, 0.82, 0.81, 0.72]
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const maxBoxDelta = 16
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const boxOrder = [5, 4, 0, 2, 1, 3]
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expect(detections.length).toEqual(6)
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detections.forEach((det, i) => {
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expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
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expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
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})
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})
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it('inputSize custom, finds all faces', async () => {
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const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
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const expectedScores = [0.89, 0.87, 0.83, 0.82, 0.81, 0.72]
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const maxBoxDelta = 16
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const boxOrder = [5, 4, 0, 2, 1, 3]
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expect(detections.length).toEqual(6)
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detections.forEach((det, i) => {
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expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
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expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
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})
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})
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})
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describeWithNets('uncompressed weights', { withTinyYolov2: { quantized: false } }, ({ tinyYolov2 }) => {
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it('inputSize lg, finds all faces', async () => {
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const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: SizeType.LG })
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const expectedScores = [0.86, 0.86, 0.85, 0.83, 0.81, 0.81]
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const maxBoxDelta = 1
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const boxOrder = [0, 1, 2, 3, 4, 5]
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expect(detections.length).toEqual(6)
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detections.forEach((det, i) => {
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expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
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expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
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})
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})
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it('inputSize md, finds all faces', async () => {
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const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: SizeType.MD })
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const expectedScores = [0.89, 0.87, 0.83, 0.83, 0.81, 0.73]
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const maxBoxDelta = 14
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const boxOrder = [5, 4, 2, 0, 1, 3]
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expect(detections.length).toEqual(6)
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detections.forEach((det, i) => {
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expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
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expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
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})
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})
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it('inputSize custom, finds all faces', async () => {
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const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
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const expectedScores = [0.89, 0.87, 0.83, 0.83, 0.81, 0.73]
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const maxBoxDelta = 14
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const boxOrder = [5, 4, 2, 0, 1, 3]
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expect(detections.length).toEqual(6)
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detections.forEach((det, i) => {
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expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
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expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
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})
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})
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})
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describe('no memory leaks', () => {
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describe('NeuralNetwork, uncompressed model', () => {
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it('disposes all param tensors', async () => {
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await expectAllTensorsReleased(async () => {
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const res = await fetch('base/weights_uncompressed/tiny_yolov2_model.weights')
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const weights = new Float32Array(await res.arrayBuffer())
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const net = faceapi.createTinyYolov2(weights)
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net.dispose()
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})
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})
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})
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describe('NeuralNetwork, quantized model', () => {
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it('disposes all param tensors', async () => {
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await expectAllTensorsReleased(async () => {
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const net = new faceapi.TinyYolov2()
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await net.load('base/weights')
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net.dispose()
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})
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})
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})
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})
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})

‎test/utils.ts

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@@ -5,7 +5,8 @@ import * as faceapi from '../src/';
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import { NeuralNetwork } from '../src/commons/NeuralNetwork';
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import { IPoint } from '../src/';
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import { allFacesFactory, allFacesMtcnnFactory } from '../src/allFacesFactory';
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import { allFacesMtcnnFunction, allFacesFunction } from '../src/globalApi';
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import { allFacesMtcnnFunction, allFacesFunction, tinyYolov2 } from '../src/globalApi';
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import { TinyYolov2 } from '../src/tinyYolov2/TinyYolov2';
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1011
export function zeros(length: number): Float32Array {
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return new Float32Array(length)
@@ -55,12 +56,13 @@ export type WithNetOptions = {
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}
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export type InjectNetArgs = {
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allFaces: allFacesFunction
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allFacesMtcnn: allFacesMtcnnFunction
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faceDetectionNet: faceapi.FaceDetectionNet
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faceLandmarkNet: faceapi.FaceLandmarkNet
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faceRecognitionNet: faceapi.FaceRecognitionNet
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mtcnn: faceapi.Mtcnn
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allFaces: allFacesFunction
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allFacesMtcnn: allFacesMtcnnFunction
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tinyYolov2: faceapi.TinyYolov2
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}
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@@ -71,6 +73,7 @@ export type DescribeWithNetsOptions = {
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withFaceLandmarkNet?: WithNetOptions
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withFaceRecognitionNet?: WithNetOptions
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withMtcnn?: WithNetOptions
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withTinyYolov2?: WithNetOptions
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}
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async function loadNetWeights(uri: string): Promise<Float32Array> {
@@ -99,17 +102,19 @@ export function describeWithNets(
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let faceLandmarkNet: faceapi.FaceLandmarkNet = new faceapi.FaceLandmarkNet()
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let faceRecognitionNet: faceapi.FaceRecognitionNet = new faceapi.FaceRecognitionNet()
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let mtcnn: faceapi.Mtcnn = new faceapi.Mtcnn()
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let tinyYolov2: faceapi.TinyYolov2 = new faceapi.TinyYolov2()
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let allFaces = allFacesFactory(faceDetectionNet, faceLandmarkNet, faceRecognitionNet)
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let allFacesMtcnn = allFacesMtcnnFactory(mtcnn, faceRecognitionNet)
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beforeAll(async () => {
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const {
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withAllFaces,
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withAllFacesMtcnn,
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withFaceDetectionNet,
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withFaceLandmarkNet,
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withFaceRecognitionNet,
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withMtcnn,
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withAllFaces,
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withAllFacesMtcnn
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withTinyYolov2
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} = options
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if (withFaceDetectionNet || withAllFaces) {
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!!withFaceDetectionNet && !withFaceDetectionNet.quantized && 'ssd_mobilenetv1_model.weights'
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)
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}
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if (withFaceLandmarkNet || withAllFaces) {
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await initNet<faceapi.FaceLandmarkNet>(
123129
faceLandmarkNet,
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'face_recognition_model.weights'
133139
)
134140
}
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if (withMtcnn || withAllFacesMtcnn) {
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await initNet<faceapi.Mtcnn>(
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mtcnn,
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!!withMtcnn && !withMtcnn.quantized && 'mtcnn_model.weights'
139146
)
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}
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if (withTinyYolov2) {
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await initNet<faceapi.TinyYolov2>(
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tinyYolov2,
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!!withTinyYolov2 && !withTinyYolov2.quantized && 'tiny_yolov2_model.weights'
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)
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}
141155
})
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afterAll(() => {
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faceDetectionNet && faceDetectionNet.dispose()
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faceLandmarkNet && faceLandmarkNet.dispose()
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faceRecognitionNet && faceRecognitionNet.dispose()
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mtcnn && mtcnn.dispose()
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mtcnn && mtcnn.dispose(),
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tinyYolov2 && tinyYolov2.dispose()
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})
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150-
specDefinitions({ faceDetectionNet, faceLandmarkNet, faceRecognitionNet, mtcnn, allFaces, allFacesMtcnn })
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specDefinitions({ allFaces, allFacesMtcnn, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, mtcnn, tinyYolov2 })
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})
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}
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