Merge pull request #13 from justadudewhohacks/unit-tests

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/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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+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceDetectionNet/index.ts"],"names":[],"mappings":";AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,cAAc,EAAE,MAAM,mBAAmB,CAAC;AAEnD,OAAO,EAAE,WAAW,EAAE,MAAM,gBAAgB,CAAC;AAE7C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,WAAW,EAAE,MAAM,eAAe,CAAC;AAC5C,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AACxD,OAAO,EAAE,WAAW,EAAE,MAAM,eAAe,CAAC;AAC5C,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AACpD,OAAO,EAAE,WAAW,EAAE,MAAM,eAAe,CAAC;AAC5C,OAAO,EAAE,IAAI,EAAE,MAAM,SAAS,CAAC;AAE/B,MAAM,2BAA2B,OAAqB;IACpD,IAAM,MAAM,GAAG,aAAa,CAAC,OAAO,CAAC,CAAA;IAErC,uBAAuB,SAAsB;QAC3C,OAAO,EAAE,CAAC,IAAI,CAAC;YAEb,IAAM,OAAO,GAAG,WAAW,CAAC,SAAS,CAAgB,CAAA;YACrD,IAAM,QAAQ,GAAG,WAAW,CAAC,OAAO,EAAE,MAAM,CAAC,kBAAkB,CAAC,CAAA;YAE1D,IAAA,mFAG4E,EAFhF,kCAAc,EACd,sCAAgB,CACgE;YAElF,OAAO,WAAW,CAAC,cAAc,EAAE,gBAAgB,EAAE,MAAM,CAAC,mBAAmB,CAAC,CAAA;QAClF,CAAC,CAAC,CAAA;IACJ,CAAC;IAED,iBAAiB,KAAuC;QACtD,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,aAAa,CAAC,WAAW,CAAC,cAAc,CAAC,KAAK,CAAC,CAAC,CAAC,EAAjD,CAAiD,CACxD,CAAA;IACH,CAAC;IAED,qBACE,KAAuC,EACvC,aAA2B,EAC3B,UAAwB;QADxB,8BAAA,EAAA,mBAA2B;QAC3B,2BAAA,EAAA,gBAAwB;;;;;;wBAGpB,oBAAoB,GAAG,CAAC,EAAE,mBAAmB,GAAG,CAAC,CAAA;wBAG/C,KAGF,EAAE,CAAC,IAAI,CAAC;4BAEV,IAAI,SAAS,GAAG,cAAc,CAAC,KAAK,CAAC,CAAA;4BAC/B,IAAA,6BAA0C,EAAzC,cAAM,EAAE,aAAK,CAA4B;4BAChD,eAAe,GAAG,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAA;4BAEnC,SAAS,GAAG,WAAW,CAAC,SAAS,CAAC,CAAA;4BAClC,oBAAoB,GAAG,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,MAAM,CAAA;4BAClD,mBAAmB,GAAG,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,KAAK,CAAA;4BAEhD,OAAO,aAAa,CAAC,SAAS,CAAC,CAAA;wBACjC,CAAC,CAAC,EAbO,MAAM,WAAA,EACL,OAAO,YAAA,CAYf;wBAGI,KAAK,GAAG,MAAM,CAAC,CAAC,CAAC,CAAA;wBACjB,MAAM,GAAG,OAAO,CAAC,CAAC,CAAC,CAAA;wBACzB,KAAS,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;4BACtC,MAAM,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;4BACnB,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;yBACrB;wBAGkB,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;wBAAC,qBAAM,MAAM,CAAC,IAAI,EAAE,EAAA;;wBAA3C,UAAU,GAAG,cAAW,SAAmB,EAAC;wBAE5C,YAAY,GAAG,GAAG,CAAA;wBAClB,OAAO,GAAG,iBAAiB,CAC/B,KAAK,EACL,UAAU,EACV,UAAU,EACV,YAAY,EACZ,aAAa,CACd,CAAA;wBAEK,OAAO,GAAG,OAAO;6BACpB,GAAG,CAAC,UAAA,GAAG;4BACA,IAAA;;;wFAGkC,EAHjC,WAAG,EAAE,cAAM,CAGsB;4BAClC,IAAA;;;uFAGiC,EAHhC,YAAI,EAAE,aAAK,CAGqB;4BACvC,OAAO,IAAI,aAAa,CACtB,UAAU,CAAC,GAAG,CAAC,EACf,IAAI,IAAI,CACN,IAAI,EACJ,GAAG,EACH,KAAK,GAAG,IAAI,EACZ,MAAM,GAAG,GAAG,CACb,EACD,eAA6B,CAC9B,CAAA;wBACH,CAAC,CAAC,CAAA;wBAEJ,KAAK,CAAC,OAAO,EAAE,CAAA;wBACf,MAAM,CAAC,OAAO,EAAE,CAAA;wBAEhB,sBAAO,OAAO,EAAA;;;;KACf;IAED,OAAO;QACL,OAAO,SAAA;QACP,WAAW,aAAA;KACZ,CAAA;AACH,CAAC"}
\ No newline at end of file

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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+{"version":3,"file":"padToSquare.js","sourceRoot":"","sources":["../src/padToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,MAAM,EAAE,MAAM,SAAS,CAAC;AAEjC;;;;;;GAMG;AACH,MAAM,sBACJ,SAAsB,EACtB,aAA8B;IAA9B,8BAAA,EAAA,qBAA8B;IAE9B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEP,IAAA,6BAA0C,EAAzC,cAAM,EAAE,aAAK,CAA4B;QAChD,IAAI,MAAM,KAAK,KAAK,EAAE;YACpB,OAAO,SAAS,CAAA;SACjB;QAED,IAAM,OAAO,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,GAAG,KAAK,CAAC,CAAA;QACxC,IAAM,aAAa,GAAG,IAAI,CAAC,KAAK,CAAC,OAAO,GAAG,CAAC,aAAa,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,WAAW,GAAG,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAE1C,IAAM,qBAAqB,GAAG,UAAC,SAA0B;YAA1B,0BAAA,EAAA,iBAA0B;YACvD,IAAM,kBAAkB,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,EAAE,CAAA;YAClD,kBAAkB,CAAC,WAAW,CAAC,GAAG,aAAa,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;YACrE,OAAO,kBAAkB,CAAA;QAC3B,CAAC,CAAA;QAED,IAAM,cAAc,GAAG,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,qBAAqB,CAAC,CAAC,MAAM,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC;aAChG,MAAM,CAAC,CAAC,SAAS,EAAG,EAAE,CAAC,IAAI,CAAC,qBAAqB,EAAE,EAAE,CAAC,CAAC,CAAC,CAAkB,CAAA;QAC7E,OAAO,EAAE,CAAC,MAAM,CAAC,cAAc,EAAE,WAAW,CAAC,CAAA;IAC/C,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ 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

+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.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

+[-0.04457738250494003, -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

+[{"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

+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

+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

+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

+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

+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

+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "module": "commonjs"
+  },
+  "include": [
+    "src",
+    "test"
+  ]
+}