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face
Commit 9cd6d13f by vincent
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unit tests for quanzized face detection and face landmark net

parent 61b702a6
Showing with 151 additions and 59 deletions
dist/faceDetectionNet/FaceDetectionNet.d.ts
......@@ -4,7 +4,7 @@ import { TNetInput } from '../types';
import { FaceDetection } from './FaceDetection';
export declare class FaceDetectionNet {
private _params;
load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
load(weightsOrUrl?: Float32Array | string): Promise<void>;
extractWeights(weights: Float32Array): void;
private forwardTensor(imgTensor);
forward(input: tf.Tensor | NetInput | TNetInput): {
......
dist/faceDetectionNet/FaceDetectionNet.js.map
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dist/faceLandmarkNet/FaceLandmarkNet.js.map
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\ No newline at end of file
src/faceDetectionNet/FaceDetectionNet.ts
......@@ -19,7 +19,7 @@ export class FaceDetectionNet {
private _params: NetParams
public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {
public async load(weightsOrUrl?: Float32Array | string): Promise<void> {
if (weightsOrUrl instanceof Float32Array) {
this.extractWeights(weightsOrUrl)
return
......
test/e2e/faceDetectionNet.test.ts
import * as faceapi from '../../src';
import { FaceDetection } from '../../src/faceDetectionNet/FaceDetection';
import { IRect } from '../../src/Rect';
import { expectMaxDelta } from '../utils';
function expectFaceDetectionEquals(result: FaceDetection, score: number, expectedBox: IRect) {
const { x, y, width, height } = result.getBox()
expect(result.getScore()).toBeCloseTo(score, 2)
expect(Math.floor(x)).toEqual(expectedBox.x)
expect(Math.floor(y)).toEqual(expectedBox.y)
expect(Math.floor(width)).toEqual(expectedBox.width)
expect(Math.floor(height)).toEqual(expectedBox.height)
function expectRectClose(
result: IRect,
expectedBox: IRect,
maxDelta: number
) {
const { x, y, width, height } = result
expectMaxDelta(x, expectedBox.x, maxDelta)
expectMaxDelta(y, expectedBox.y, maxDelta)
expectMaxDelta(width, expectedBox.width, maxDelta)
expectMaxDelta(height, expectedBox.height, maxDelta)
}
const expectedBoxes = [
{ x: 48, y: 253, width: 104, height: 129 },
{ x: 260, y: 227, width: 76, height: 117 },
{ x: 466, y: 165, width: 88, height: 130 },
{ x: 234, y: 36, width: 84, height: 119 },
{ x: 577, y: 65, width: 84, height: 105 },
{ x: 84, y: 14, width: 79, height: 132 }
]
describe('faceDetectionNet', () => {
let faceDetectionNet: faceapi.FaceDetectionNet, imgEl: HTMLImageElement
let imgEl: HTMLImageElement
beforeAll(async () => {
const res = await fetch('base/weights/uncompressed/face_detection_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
faceDetectionNet = faceapi.faceDetectionNet(weights)
const img = await (await fetch('base/test/images/faces.jpg')).blob()
imgEl = await faceapi.bufferToImage(img)
})
it('scores > 0.8', async () => {
const { width, height } = imgEl
describe('uncompressed weights', () => {
let faceDetectionNet: faceapi.FaceDetectionNet
const expectedScores = [0.98, 0.89, 0.82, 0.75, 0.58, 0.55]
const maxBoxDelta = 1
beforeAll(async () => {
const res = await fetch('base/weights/uncompressed/face_detection_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
faceDetectionNet = faceapi.faceDetectionNet(weights)
})
const result = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
expect(result.length).toEqual(3)
it('scores > 0.8', async () => {
const detections = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
result.forEach(res => {
expect(res.getImageWidth()).toEqual(width)
expect(res.getImageHeight()).toEqual(height)
expect(detections.length).toEqual(3)
detections.forEach((det, i) => {
expect(det.getImageWidth()).toEqual(imgEl.width)
expect(det.getImageHeight()).toEqual(imgEl.height)
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedBoxes[i], maxBoxDelta)
})
})
const [d0, d1, d2] = result
expectFaceDetectionEquals(d0, 0.98, { x: 48, y: 253, width: 104, height: 129 })
expectFaceDetectionEquals(d1, 0.89, { x: 260, y: 227, width: 76, height: 117 })
expectFaceDetectionEquals(d2, 0.82, { x: 466, y: 165, width: 88, height: 130 })
it('scores > 0.5', async () => {
const detections = await faceDetectionNet.locateFaces(imgEl, 0.5) as FaceDetection[]
expect(detections.length).toEqual(6)
detections.forEach((det, i) => {
expect(det.getImageWidth()).toEqual(imgEl.width)
expect(det.getImageHeight()).toEqual(imgEl.height)
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedBoxes[i], maxBoxDelta)
})
})
})
it('scores > 0.5', async () => {
const { width, height } = imgEl
describe('quantized weights', () => {
const result = await faceDetectionNet.locateFaces(imgEl, 0.5) as FaceDetection[]
expect(result.length).toEqual(6)
let faceDetectionNet: faceapi.FaceDetectionNet
result.forEach(res => {
expect(res.getImageWidth()).toEqual(width)
expect(res.getImageHeight()).toEqual(height)
const expectedScores = [0.97, 0.88, 0.83, 0.82, 0.59, 0.52]
const maxBoxDelta = 5
beforeAll(async () => {
faceDetectionNet = new faceapi.FaceDetectionNet()
await faceDetectionNet.load('base/weights')
})
const [d0, d1, d2, d3, d4, d5] = result
expectFaceDetectionEquals(d0, 0.98, { x: 48, y: 253, width: 104, height: 129 })
expectFaceDetectionEquals(d1, 0.89, { x: 260, y: 227, width: 76, height: 117 })
expectFaceDetectionEquals(d2, 0.82, { x: 466, y: 165, width: 88, height: 130 })
expectFaceDetectionEquals(d3, 0.75, { x: 234, y: 36, width: 84, height: 119 })
expectFaceDetectionEquals(d4, 0.58, { x: 577, y: 65, width: 84, height: 105 })
expectFaceDetectionEquals(d5, 0.55, { x: 84, y: 14, width: 79, height: 132 })
it('scores > 0.8', async () => {
const detections = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
expect(detections.length).toEqual(4)
detections.forEach((det, i) => {
expect(det.getImageWidth()).toEqual(imgEl.width)
expect(det.getImageHeight()).toEqual(imgEl.height)
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedBoxes[i], maxBoxDelta)
})
})
it('scores > 0.5', async () => {
const detections = await faceDetectionNet.locateFaces(imgEl, 0.5) as FaceDetection[]
expect(detections.length).toEqual(6)
detections.forEach((det, i) => {
expect(det.getImageWidth()).toEqual(imgEl.width)
expect(det.getImageHeight()).toEqual(imgEl.height)
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedBoxes[i], maxBoxDelta)
})
})
})
})
\ No newline at end of file
test/e2e/faceLandmarkNet.test.ts
import * as faceapi from '../../src';
import { FaceLandmarks } from '../../src/faceLandmarkNet/FaceLandmarks';
import { Point } from '../../src/Point';
import { expectMaxDelta } from '../utils';
describe('faceLandmarkNet', () => {
let faceLandmarkNet: faceapi.FaceLandmarkNet, imgEl: HTMLImageElement, faceLandmarkPositions: Point[]
let imgEl: HTMLImageElement
beforeAll(async () => {
const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
faceLandmarkNet = faceapi.faceLandmarkNet(weights)
const img = await (await fetch('base/test/images/face.png')).blob()
imgEl = await faceapi.bufferToImage(img)
faceLandmarkPositions = await (await fetch('base/test/data/faceLandmarkPositions.json')).json()
})
it('computes face descriptor', async () => {
const { width, height } = imgEl
describe('uncompressed weights', () => {
let faceLandmarkNet: faceapi.FaceLandmarkNet, faceLandmarkPositions: Point[]
beforeAll(async () => {
const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
faceLandmarkNet = faceapi.faceLandmarkNet(weights)
faceLandmarkPositions = await (await fetch('base/test/data/faceLandmarkPositions.json')).json()
})
it('computes face landmarks', async () => {
const { width, height } = imgEl
const result = await faceLandmarkNet.detectLandmarks(imgEl) as FaceLandmarks
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
expect(result.getPositions().map(({ x, y }) => ({ x, y }))).toEqual(faceLandmarkPositions)
})
const result = await faceLandmarkNet.detectLandmarks(imgEl) as FaceLandmarks
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
expect(result.getPositions().map(({ x, y }) => ({ x, y }))).toEqual(faceLandmarkPositions)
})
describe('quantized weights', () => {
let faceLandmarkNet: faceapi.FaceLandmarkNet, faceLandmarkPositions: Point[]
beforeAll(async () => {
faceLandmarkNet = new faceapi.FaceLandmarkNet()
await faceLandmarkNet.load('base/weights')
faceLandmarkPositions = await (await fetch('base/test/data/faceLandmarkPositions.json')).json()
})
it('computes face landmarks', async () => {
const { width, height } = imgEl
const result = await faceLandmarkNet.detectLandmarks(imgEl) as FaceLandmarks
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach(({ x, y }, i) => {
expectMaxDelta(x, faceLandmarkPositions[i].x, 2)
expectMaxDelta(y, faceLandmarkPositions[i].y, 2)
})
})
})
})
test/utils.ts
......@@ -4,4 +4,8 @@ export function zeros(length: number): Float32Array {
export function ones(length: number): Float32Array {
return new Float32Array(length).fill(1)
}
\ No newline at end of file
}
export function expectMaxDelta(val1: number, val2: number, maxDelta: number) {
expect(Math.abs(val1 - val2)).toBeLessThan(maxDelta)
}
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