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Unverified Commit 9db970d6 by justadudewhohacks Committed by GitHub
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Merge pull request #13 from justadudewhohacks/unit-tests

some unit and end to end tests
parents be40997b 01e37211
Showing with 311 additions and 21 deletions
dist/Rect.d.ts
export declare class Rect {
export interface IRect {
x: number;
y: number;
width: number;
height: number;
}
export declare class Rect implements IRect {
x: number;
y: number;
width: number;
......
dist/Rect.js.map
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\ No newline at end of file
dist/face-api.js
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dist/face-api.min.js
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dist/faceDetectionNet/index.d.ts
......@@ -6,5 +6,5 @@ export declare function faceDetectionNet(weights: Float32Array): {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
locateFaces: (input: tf.Tensor<tf.Rank> | NetInput, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
locateFaces: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
};
dist/faceDetectionNet/index.js.map
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dist/padToSquare.js
import * as tf from '@tensorflow/tfjs-core';
import { isEven } from './utils';
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
......@@ -13,12 +14,17 @@ export function padToSquare(imgTensor, isCenterImage) {
if (height === width) {
return imgTensor;
}
var paddingAmount = Math.floor(Math.abs(height - width) * (isCenterImage ? 0.5 : 1));
var dimDiff = Math.abs(height - width);
var paddingAmount = Math.floor(dimDiff * (isCenterImage ? 0.5 : 1));
var paddingAxis = height > width ? 2 : 1;
var paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount;
var tensorsToStack = (isCenterImage ? [tf.fill(paddingTensorShape, 0)] : [])
.concat([imgTensor, tf.fill(paddingTensorShape, 0)]);
var getPaddingTensorShape = function (isRoundUp) {
if (isRoundUp === void 0) { isRoundUp = false; }
var paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount + (isRoundUp ? 1 : 0);
return paddingTensorShape;
};
var tensorsToStack = (isCenterImage ? [tf.fill(getPaddingTensorShape(!isEven(dimDiff)), 0)] : [])
.concat([imgTensor, tf.fill(getPaddingTensorShape(), 0)]);
return tf.concat(tensorsToStack, paddingAxis);
});
}
......
dist/padToSquare.js.map
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\ No newline at end of file
dist/utils.d.ts
......@@ -3,6 +3,7 @@ import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { Dimensions, DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
export declare function isFloat(num: number): boolean;
export declare function isEven(num: number): boolean;
export declare function round(num: number): number;
export declare function getElement(arg: string | any): any;
export declare function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D;
......
dist/utils.js
......@@ -3,6 +3,9 @@ import * as tf from '@tensorflow/tfjs-core';
export function isFloat(num) {
return num % 1 !== 0;
}
export function isEven(num) {
return num % 2 === 0;
}
export function round(num) {
return Math.floor(num * 100) / 100;
}
......
karma.conf.js 0 → 100644
const dataFiles = [
'test/images/*.jpg',
'test/images/*.png',
'test/data/*.json',
'weights/*.weights'
].map(pattern => ({
pattern,
watched: false,
included: false,
served: true,
nocache: false
}))
module.exports = function(config) {
config.set({
frameworks: ['jasmine', 'karma-typescript'],
files: [
'src/**/*.ts',
'test/**/*.ts'
].concat(dataFiles),
preprocessors: {
'**/*.ts': ['karma-typescript']
},
karmaTypescriptConfig: {
tsconfig: 'tsconfig.test.json'
},
browsers: ['Chrome']
})
}
package-lock.json
This source diff could not be displayed because it is too large. You can view the blob instead.
package.json
......@@ -7,7 +7,8 @@
"scripts": {
"rollup-min": "rollup -c rollup.config.js --environment minify:true",
"rollup": "rollup -c rollup.config.js",
"build": "npm run rollup && npm run rollup-min && tsc"
"build": "npm run rollup && npm run rollup-min && tsc",
"test": "karma start"
},
"keywords": [
"face",
......@@ -22,7 +23,15 @@
"@tensorflow/tfjs-core": "^0.11.0"
},
"devDependencies": {
"@types/axios": "^0.14.0",
"@types/jasmine": "^2.8.8",
"@types/node": "^10.1.1",
"axios": "^0.18.0",
"jasmine-core": "^3.1.0",
"karma": "^2.0.3",
"karma-chrome-launcher": "^2.2.0",
"karma-jasmine": "^1.1.2",
"karma-typescript": "^3.0.12",
"rollup": "^0.59.1",
"rollup-plugin-commonjs": "^9.1.3",
"rollup-plugin-node-resolve": "^3.3.0",
......
src/Rect.ts
export class Rect {
export interface IRect {
x: number
y: number
width: number
height: number
}
export class Rect implements IRect {
public x: number
public y: number
public width: number
......
src/faceDetectionNet/index.ts
......@@ -38,7 +38,7 @@ export function faceDetectionNet(weights: Float32Array) {
}
async function locateFaces(
input: tf.Tensor | NetInput,
input: tf.Tensor | NetInput | TNetInput,
minConfidence: number = 0.8,
maxResults: number = 100,
): Promise<FaceDetection[]> {
......
src/padToSquare.ts
import * as tf from '@tensorflow/tfjs-core';
import { isEven } from './utils';
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
......@@ -18,13 +20,18 @@ export function padToSquare(
return imgTensor
}
const paddingAmount = Math.floor(Math.abs(height - width) * (isCenterImage ? 0.5 : 1))
const dimDiff = Math.abs(height - width)
const paddingAmount = Math.floor(dimDiff * (isCenterImage ? 0.5 : 1))
const paddingAxis = height > width ? 2 : 1
const paddingTensorShape = imgTensor.shape.slice() as [number, number, number, number]
paddingTensorShape[paddingAxis] = paddingAmount
const tensorsToStack = (isCenterImage ? [tf.fill(paddingTensorShape, 0)] : [])
.concat([imgTensor, tf.fill(paddingTensorShape, 0)]) as tf.Tensor4D[]
const getPaddingTensorShape = (isRoundUp: boolean = false): number[] => {
const paddingTensorShape = imgTensor.shape.slice()
paddingTensorShape[paddingAxis] = paddingAmount + (isRoundUp ? 1 : 0)
return paddingTensorShape
}
const tensorsToStack = (isCenterImage ? [tf.fill(getPaddingTensorShape(!isEven(dimDiff)), 0)] : [])
.concat([imgTensor, tf.fill(getPaddingTensorShape(), 0)]) as tf.Tensor4D[]
return tf.concat(tensorsToStack, paddingAxis)
})
}
\ No newline at end of file
src/utils.ts
......@@ -9,6 +9,10 @@ export function isFloat(num: number) {
return num % 1 !== 0
}
export function isEven(num: number) {
return num % 2 === 0
}
export function round(num: number) {
return Math.floor(num * 100) / 100
}
......
test/data/faceDescriptor.json 0 → 100644
[-0.04457738250494003, -0.043990395963191986, -0.025750599801540375, 0.016651155427098274, -0.05218124762177467, -0.05193494260311127, 0.0099308080971241, -0.016631772741675377, 0.1175108402967453, 0.04090115427970886, 0.22149613499641418, -0.08035797625780106, -0.23618969321250916, -0.02510468289256096, -0.02730529010295868, 0.10455037653446198, -0.12426768243312836, -0.0886617973446846, -0.14048148691654205, -0.040384188294410706, -0.041014235466718674, 0.11965534090995789, -0.015900742262601852, 0.08545806258916855, -0.10701242089271545, -0.2919914722442627, -0.09832733124494553, -0.15806683897972107, 0.007252881303429604, -0.09897305816411972, 0.02866348624229431, 0.17405937612056732, -0.17985646426677704, -0.11151747405529022, 0.1291065365076065, 0.023578189313411713, -0.04872015863656998, -0.023027952760457993, 0.24042242765426636, 0.09909330308437347, -0.14796072244644165, -0.034880466759204865, 0.023121334612369537, 0.35197964310646057, 0.13432590663433075, 0.025992823764681816, 0.027310356497764587, -0.07258806377649307, 0.09854947030544281, -0.2654016613960266, 0.09129738807678223, 0.18322113156318665, 0.14081577956676483, 0.08943498134613037, 0.07813337445259094, -0.1866082400083542, 0.012191163375973701, 0.13849540054798126, -0.1988903284072876, 0.14307452738285065, 0.04158562421798706, -0.0641125738620758, -0.0019705239683389664, -0.10619331151247025, 0.1386488378047943, 0.019395820796489716, -0.10675815492868423, -0.06835387647151947, 0.19760596752166748, -0.10146244615316391, -0.005424058996140957, 0.13664256036281586, -0.10927096754312515, -0.2449037879705429, -0.2788461446762085, 0.025820128619670868, 0.40477481484413147, 0.08949816972017288, -0.2150505781173706, -0.00494978204369545, -0.05536277964711189, -0.06972620636224747, -0.008085237815976143, 0.027283620089292526, -0.0860084593296051, 0.017455315217375755, -0.07179906964302063, 0.04873117804527283, 0.20279690623283386, 0.011654914356768131, -0.007634453475475311, 0.1842338889837265, -0.06324955075979233, -0.07316181063652039, 0.122040756046772, 0.08807040750980377, -0.0505395382642746, -0.08226525038480759, -0.11426772177219391, 0.000004230067133903503, 0.02770165540277958, -0.2017858773469925, -0.02197061851620674, 0.056306831538677216, -0.17306528985500336, 0.20115001499652863, 0.008511766791343689, -0.07041959464550018, -0.08531103283166885, -0.043383363634347916, -0.07298384606838226, 0.07277849316596985, 0.25687724351882935, -0.24525471031665802, 0.21955141425132751, 0.07719043642282486, 0.011913364753127098, 0.17791582643985748, -0.057796183973550797, 0.10519653558731079, -0.15350784361362457, -0.09388457238674164, -0.1587708741426468, -0.14040742814540863, 0.035962484776973724, 0.040929798036813736, 0.04888196289539337, 0.0146100465208292]
\ No newline at end of file
test/data/faceLandmarkPositions.json 0 → 100644
[{"x": 15.145603939890862, "y": 42.00849384069443}, {"x": 16.228551417589188, "y": 58.02471041679382}, {"x": 18.641504645347595, "y": 73.78255426883698}, {"x": 21.99183627963066, "y": 89.28244113922119}, {"x": 27.241109311580658, "y": 103.89149487018585}, {"x": 35.77959090471268, "y": 117.20520257949829}, {"x": 47.162988781929016, "y": 128.52083444595337}, {"x": 60.60639023780823, "y": 137.3870998620987}, {"x": 76.22466087341309, "y": 139.8476779460907}, {"x": 91.47888422012329, "y": 136.8298441171646}, {"x": 104.65206205844879, "y": 127.64202654361725}, {"x": 115.92219471931458, "y": 116.16075038909912}, {"x": 124.51052069664001, "y": 102.82219648361206}, {"x": 129.76784706115723, "y": 87.81199157238007}, {"x": 132.6519101858139, "y": 72.15077430009842}, {"x": 134.51203107833862, "y": 56.30408674478531}, {"x": 135.33487915992737, "y": 40.10292738676071}, {"x": 24.932710826396942, "y": 32.96273946762085}, {"x": 32.57601857185364, "y": 28.00302878022194}, {"x": 42.381374537944794, "y": 27.10871994495392}, {"x": 52.198585867881775, "y": 29.124750941991806}, {"x": 61.5922674536705, "y": 32.90563523769379}, {"x": 85.18133461475372, "y": 31.272751092910767}, {"x": 94.89730596542358, "y": 26.85772553086281}, {"x": 105.1171749830246, "y": 24.783611297607422}, {"x": 115.2367115020752, "y": 25.725752860307693}, {"x": 123.13878536224365, "y": 30.079219490289688}, {"x": 73.67254793643951, "y": 43.12345236539841}, {"x": 73.7981915473938, "y": 55.55519610643387}, {"x": 73.8839328289032, "y": 67.69467741250992}, {"x": 73.96439015865326, "y": 79.91722226142883}, {"x": 62.394945323467255, "y": 85.0008487701416}, {"x": 68.01678389310837, "y": 87.24332749843597}, {"x": 74.27898198366165, "y": 88.8382226228714}, {"x": 80.42970299720764, "y": 86.84954345226288}, {"x": 85.87614297866821, "y": 84.31319296360016}, {"x": 35.7684463262558, "y": 44.57894414663315}, {"x": 42.30532944202423, "y": 41.117119789123535}, {"x": 50.501713156700134, "y": 41.50397926568985}, {"x": 56.56731426715851, "y": 45.83812057971954}, {"x": 50.084468722343445, "y": 48.037388920784}, {"x": 41.91972613334656, "y": 47.92611300945282}, {"x": 91.28680229187012, "y": 44.677162170410156}, {"x": 97.76994287967682, "y": 40.084308385849}, {"x": 105.72623312473297, "y": 39.8311972618103}, {"x": 111.95003092288971, "y": 42.97384321689606}, {"x": 106.4166247844696, "y": 46.30742222070694}, {"x": 98.37331473827362, "y": 46.70642763376236}, {"x": 51.94934159517288, "y": 102.49188244342804}, {"x": 60.71081757545471, "y": 101.18674635887146}, {"x": 68.67328137159348, "y": 100.15718936920166}, {"x": 74.38371777534485, "y": 101.07522904872894}, {"x": 80.5251270532608, "y": 99.71560835838318}, {"x": 88.56176733970642, "y": 100.32560527324677}, {"x": 96.980881690979, "y": 100.81177353858948}, {"x": 89.01146650314331, "y": 108.55080485343933}, {"x": 81.3762366771698, "y": 112.2200220823288}, {"x": 74.73004907369614, "y": 113.34742605686188}, {"x": 68.42194050550461, "y": 112.92429864406586}, {"x": 60.53387671709061, "y": 110.02114713191986}, {"x": 55.876149237155914, "y": 103.3714234828949}, {"x": 68.54872852563858, "y": 104.03825640678406}, {"x": 74.44304376840591, "y": 104.46503162384033}, {"x": 80.63703775405884, "y": 103.56011688709259}, {"x": 93.1486040353775, "y": 102.0165503025055}, {"x": 80.84633946418762, "y": 104.64600920677185}, {"x": 74.63692724704742, "y": 105.62551617622375}, {"x": 68.74832957983017, "y": 105.22661805152893}]
\ No newline at end of file
test/e2e/faceDetectionNet.test.ts 0 → 100644
import axios from 'axios';
import * as faceapi from '../../src';
import { FaceDetection } from '../../src/faceDetectionNet/FaceDetection';
import { IRect } from '../../src/Rect';
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)
}
describe('faceDetectionNet', () => {
let faceDetectionNet: any, imgEl: HTMLImageElement
beforeAll(async () => {
const res = await axios.get('base/weights/face_detection_model.weights', { responseType: 'arraybuffer' })
const weights = new Float32Array(res.data)
faceDetectionNet = faceapi.faceDetectionNet(weights)
const img = await axios.get('base/test/images/faces.jpg', { responseType: 'blob' })
imgEl = await faceapi.bufferToImage(img.data)
})
it('scores > 0.9', async () => {
const { width, height } = imgEl
const result = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
expect(result.length).toEqual(3)
result.forEach(res => {
expect(res.getImageWidth()).toEqual(width)
expect(res.getImageHeight()).toEqual(height)
})
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 { width, height } = imgEl
const result = await faceDetectionNet.locateFaces(imgEl, 0.5) as FaceDetection[]
expect(result.length).toEqual(6)
result.forEach(res => {
expect(res.getImageWidth()).toEqual(width)
expect(res.getImageHeight()).toEqual(height)
})
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 })
})
})
\ No newline at end of file
test/e2e/faceLandmarkNet.test.ts 0 → 100644
import axios from 'axios';
import * as faceapi from '../../src';
import { FaceLandmarks } from '../../src/faceLandmarkNet/FaceLandmarks';
import { Point } from '../../src/Point';
describe('faceLandmarkNet', () => {
let faceLandmarkNet: any, imgEl: HTMLImageElement, faceLandmarkPositions: Point[]
beforeAll(async () => {
const res = await axios.get('base/weights/face_landmark_68_model.weights', { responseType: 'arraybuffer' })
const weights = new Float32Array(res.data)
faceLandmarkNet = faceapi.faceLandmarkNet(weights)
const img = await axios.get('base/test/images/face.png', { responseType: 'blob' })
imgEl = await faceapi.bufferToImage(img.data)
faceLandmarkPositions = (await axios.get('base/test/data/faceLandmarkPositions.json')).data
})
it('computes face descriptor', 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)
})
})
test/e2e/faceRecognitionNet.test.ts 0 → 100644
import axios from 'axios';
import * as faceapi from '../../src';
describe('faceRecognitionNet', () => {
let faceRecognitionNet: any, imgEl: HTMLImageElement, faceDescriptor: number[]
beforeAll(async () => {
const res = await axios.get('base/weights/face_recognition_model.weights', { responseType: 'arraybuffer' })
const weights = new Float32Array(res.data)
faceRecognitionNet = faceapi.faceRecognitionNet(weights)
const img = await axios.get('base/test/images/face.png', { responseType: 'blob' })
imgEl = await faceapi.bufferToImage(img.data)
faceDescriptor = (await axios.get('base/test/data/faceDescriptor.json')).data
})
it('computes face descriptor', async () => {
const result = await faceRecognitionNet.computeFaceDescriptor(imgEl) as number[]
expect(result.length).toEqual(128)
expect(result).toEqual(new Float32Array(faceDescriptor))
})
})
\ No newline at end of file
test/padToSquare.test.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { padToSquare } from '../src/padToSquare';
import { ones, zeros } from './utils';
describe('padToSquare', () => {
describe('even size', () => {
it('is padded to square', () => tf.tidy(() => {
const imgTensor = tf.tensor4d(Array(24).fill(1), [1, 4, 2, 3])
const result = padToSquare(imgTensor)
expect(result.shape).toEqual([1, 4, 4, 3])
const paddedCols = tf.unstack(result, 2)
expect(paddedCols.length).toEqual(4)
expect(paddedCols[0].dataSync()).toEqual(ones(12))
expect(paddedCols[1].dataSync()).toEqual(ones(12))
expect(paddedCols[2].dataSync()).toEqual(zeros(12))
expect(paddedCols[3].dataSync()).toEqual(zeros(12))
}))
it('is padded to square and centered', () => tf.tidy(() => {
const imgTensor = tf.tensor4d(Array(24).fill(1), [1, 4, 2, 3])
const result = padToSquare(imgTensor, true)
expect(result.shape).toEqual([1, 4, 4, 3])
const paddedCols = tf.unstack(result, 2)
expect(paddedCols.length).toEqual(4)
expect(paddedCols[0].dataSync()).toEqual(zeros(12))
expect(paddedCols[1].dataSync()).toEqual(ones(12))
expect(paddedCols[2].dataSync()).toEqual(ones(12))
expect(paddedCols[3].dataSync()).toEqual(zeros(12))
}))
})
describe('uneven size', () => {
it('is padded to square', () => tf.tidy(() => {
const imgTensor = tf.tensor4d(Array(30).fill(1), [1, 5, 2, 3])
const result = padToSquare(imgTensor)
expect(result.shape).toEqual([1, 5, 5, 3])
const paddedCols = tf.unstack(result, 2)
expect(paddedCols.length).toEqual(5)
expect(paddedCols[0].dataSync()).toEqual(ones(15))
expect(paddedCols[1].dataSync()).toEqual(ones(15))
expect(paddedCols[2].dataSync()).toEqual(zeros(15))
expect(paddedCols[3].dataSync()).toEqual(zeros(15))
expect(paddedCols[4].dataSync()).toEqual(zeros(15))
}))
it('is padded to square and centered', () => tf.tidy(() => {
const imgTensor = tf.tensor4d(Array(30).fill(1), [1, 5, 2, 3])
const result = padToSquare(imgTensor, true)
expect(result.shape).toEqual([1, 5, 5, 3])
const data = result.dataSync()
const paddedCols = tf.unstack(result, 2)
expect(paddedCols.length).toEqual(5)
expect(paddedCols[0].dataSync()).toEqual(zeros(15))
expect(paddedCols[1].dataSync()).toEqual(zeros(15))
expect(paddedCols[2].dataSync()).toEqual(ones(15))
expect(paddedCols[3].dataSync()).toEqual(ones(15))
expect(paddedCols[4].dataSync()).toEqual(zeros(15))
}))
})
})
test/utils.ts 0 → 100644
export function zeros(length: number): Float32Array {
return new Float32Array(length)
}
export function ones(length: number): Float32Array {
return new Float32Array(length).fill(1)
}
\ No newline at end of file
tsconfig.test.json 0 → 100644
{
"extends": "./tsconfig.json",
"compilerOptions": {
"module": "commonjs"
},
"include": [
"src",
"test"
]
}
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