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remove build directory

parent fc8a27e1
Showing with 2 additions and 1494 deletions
.gitignore
......@@ -7,5 +7,4 @@ proto
weights_uncompressed
weights_unused
docs
out
build
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build/commonjs/ageGenderNet/AgeGenderNet.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { TinyXception } from '../xception/TinyXception';
import { AgeAndGenderPrediction, NetOutput, NetParams } from './types';
export declare class AgeGenderNet extends NeuralNetwork<NetParams> {
private _faceFeatureExtractor;
constructor(faceFeatureExtractor?: TinyXception);
readonly faceFeatureExtractor: TinyXception;
runNet(input: NetInput | tf.Tensor4D): NetOutput;
forwardInput(input: NetInput | tf.Tensor4D): NetOutput;
forward(input: TNetInput): Promise<NetOutput>;
predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]>;
protected getDefaultModelName(): string;
dispose(throwOnRedispose?: boolean): void;
loadClassifierParams(weights: Float32Array): void;
extractClassifierParams(weights: Float32Array): {
params: NetParams;
paramMappings: import("tfjs-image-recognition-base/build/commonjs/common").ParamMapping[];
};
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: import("tfjs-image-recognition-base/build/commonjs/common").ParamMapping[];
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: import("tfjs-image-recognition-base/build/commonjs/common").ParamMapping[];
};
}
build/commonjs/ageGenderNet/AgeGenderNet.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var fullyConnectedLayer_1 = require("../common/fullyConnectedLayer");
var util_1 = require("../faceProcessor/util");
var TinyXception_1 = require("../xception/TinyXception");
var extractParams_1 = require("./extractParams");
var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
var types_1 = require("./types");
var AgeGenderNet = /** @class */ (function (_super) {
tslib_1.__extends(AgeGenderNet, _super);
function AgeGenderNet(faceFeatureExtractor) {
if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new TinyXception_1.TinyXception(2); }
var _this = _super.call(this, 'AgeGenderNet') || this;
_this._faceFeatureExtractor = faceFeatureExtractor;
return _this;
}
Object.defineProperty(AgeGenderNet.prototype, "faceFeatureExtractor", {
get: function () {
return this._faceFeatureExtractor;
},
enumerable: true,
configurable: true
});
AgeGenderNet.prototype.runNet = function (input) {
var _this = this;
var params = this.params;
if (!params) {
throw new Error(this._name + " - load model before inference");
}
return tf.tidy(function () {
var bottleneckFeatures = input instanceof tfjs_image_recognition_base_1.NetInput
? _this.faceFeatureExtractor.forwardInput(input)
: input;
var pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);
var age = fullyConnectedLayer_1.fullyConnectedLayer(pooled, params.fc.age).as1D();
var gender = fullyConnectedLayer_1.fullyConnectedLayer(pooled, params.fc.gender);
return { age: age, gender: gender };
});
};
AgeGenderNet.prototype.forwardInput = function (input) {
var _this = this;
return tf.tidy(function () {
var _a = _this.runNet(input), age = _a.age, gender = _a.gender;
return { age: age, gender: tf.softmax(gender) };
});
};
AgeGenderNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
AgeGenderNet.prototype.predictAgeAndGender = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, out, ages, genders, ageAndGenderTensors, predictionsByBatch;
var _this = this;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _a.sent();
return [4 /*yield*/, this.forwardInput(netInput)];
case 2:
out = _a.sent();
ages = tf.unstack(out.age);
genders = tf.unstack(out.gender);
ageAndGenderTensors = ages.map(function (ageTensor, i) { return ({
ageTensor: ageTensor,
genderTensor: genders[i]
}); });
return [4 /*yield*/, Promise.all(ageAndGenderTensors.map(function (_a) {
var ageTensor = _a.ageTensor, genderTensor = _a.genderTensor;
return tslib_1.__awaiter(_this, void 0, void 0, function () {
var age, probMale, isMale, gender, genderProbability;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, ageTensor.data()];
case 1:
age = (_b.sent())[0];
return [4 /*yield*/, genderTensor.data()];
case 2:
probMale = (_b.sent())[0];
isMale = probMale > 0.5;
gender = isMale ? types_1.Gender.MALE : types_1.Gender.FEMALE;
genderProbability = isMale ? probMale : (1 - probMale);
ageTensor.dispose();
genderTensor.dispose();
return [2 /*return*/, { age: age, gender: gender, genderProbability: genderProbability }];
}
});
});
}))];
case 3:
predictionsByBatch = _a.sent();
out.age.dispose();
out.gender.dispose();
return [2 /*return*/, netInput.isBatchInput
? predictionsByBatch
: predictionsByBatch[0]];
}
});
});
};
AgeGenderNet.prototype.getDefaultModelName = function () {
return 'age_gender_model';
};
AgeGenderNet.prototype.dispose = function (throwOnRedispose) {
if (throwOnRedispose === void 0) { throwOnRedispose = true; }
this.faceFeatureExtractor.dispose(throwOnRedispose);
_super.prototype.dispose.call(this, throwOnRedispose);
};
AgeGenderNet.prototype.loadClassifierParams = function (weights) {
var _a = this.extractClassifierParams(weights), params = _a.params, paramMappings = _a.paramMappings;
this._params = params;
this._paramMappings = paramMappings;
};
AgeGenderNet.prototype.extractClassifierParams = function (weights) {
return extractParams_1.extractParams(weights);
};
AgeGenderNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
var _a = util_1.seperateWeightMaps(weightMap), featureExtractorMap = _a.featureExtractorMap, classifierMap = _a.classifierMap;
this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(classifierMap);
};
AgeGenderNet.prototype.extractParams = function (weights) {
var classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);
var featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
var classifierWeights = weights.slice(weights.length - classifierWeightSize);
this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
return this.extractClassifierParams(classifierWeights);
};
return AgeGenderNet;
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.AgeGenderNet = AgeGenderNet;
//# sourceMappingURL=AgeGenderNet.js.map
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build/commonjs/ageGenderNet/AgeGenderNet.js.map deleted 100644 → 0
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build/commonjs/ageGenderNet/extractParams.d.ts deleted 100644 → 0
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: TfjsImageRecognitionBase.ParamMapping[];
};
build/commonjs/ageGenderNet/extractParams.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
function extractParams(weights) {
var paramMappings = [];
var _a = tfjs_image_recognition_base_1.TfjsImageRecognitionBase.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractFCParams = tfjs_image_recognition_base_1.TfjsImageRecognitionBase.extractFCParamsFactory(extractWeights, paramMappings);
var age = extractFCParams(512, 1, 'fc/age');
var gender = extractFCParams(512, 2, 'fc/gender');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
paramMappings: paramMappings,
params: { fc: { age: age, gender: gender } }
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/commonjs/ageGenderNet/extractParams.js.map deleted 100644 → 0
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build/commonjs/ageGenderNet/extractParamsFromWeigthMap.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
import { NetParams } from './types';
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: TfjsImageRecognitionBase.ParamMapping[];
};
build/commonjs/ageGenderNet/extractParamsFromWeigthMap.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
function extractParamsFromWeigthMap(weightMap) {
var paramMappings = [];
var extractWeightEntry = tfjs_image_recognition_base_1.TfjsImageRecognitionBase.extractWeightEntryFactory(weightMap, paramMappings);
function extractFcParams(prefix) {
var weights = extractWeightEntry(prefix + "/weights", 2);
var bias = extractWeightEntry(prefix + "/bias", 1);
return { weights: weights, bias: bias };
}
var params = {
fc: {
age: extractFcParams('fc/age'),
gender: extractFcParams('fc/gender')
}
};
tfjs_image_recognition_base_1.TfjsImageRecognitionBase.disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
exports.extractParamsFromWeigthMap = extractParamsFromWeigthMap;
//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
build/commonjs/ageGenderNet/extractParamsFromWeigthMap.js.map deleted 100644 → 0
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build/commonjs/ageGenderNet/index.d.ts deleted 100644 → 0
export * from './AgeGenderNet';
export * from './types';
build/commonjs/ageGenderNet/index.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./AgeGenderNet"), exports);
tslib_1.__exportStar(require("./types"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/ageGenderNet/index.js.map deleted 100644 → 0
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build/commonjs/ageGenderNet/types.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
export declare type AgeAndGenderPrediction = {
age: number;
gender: Gender;
genderProbability: number;
};
export declare enum Gender {
FEMALE = "female",
MALE = "male"
}
export declare type NetOutput = {
age: tf.Tensor1D;
gender: tf.Tensor2D;
};
export declare type NetParams = {
fc: {
age: TfjsImageRecognitionBase.FCParams;
gender: TfjsImageRecognitionBase.FCParams;
};
};
build/commonjs/ageGenderNet/types.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Gender;
(function (Gender) {
Gender["FEMALE"] = "female";
Gender["MALE"] = "male";
})(Gender = exports.Gender || (exports.Gender = {}));
//# sourceMappingURL=types.js.map
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build/commonjs/ageGenderNet/types.js.map deleted 100644 → 0
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build/commonjs/classes/FaceDetection.d.ts deleted 100644 → 0
import { Box, IDimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
export interface IFaceDetecion {
score: number;
box: Box;
}
export declare class FaceDetection extends ObjectDetection implements IFaceDetecion {
constructor(score: number, relativeBox: Rect, imageDims: IDimensions);
forSize(width: number, height: number): FaceDetection;
}
build/commonjs/classes/FaceDetection.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection = /** @class */ (function (_super) {
tslib_1.__extends(FaceDetection, _super);
function FaceDetection(score, relativeBox, imageDims) {
return _super.call(this, score, score, '', relativeBox, imageDims) || this;
}
FaceDetection.prototype.forSize = function (width, height) {
var _a = _super.prototype.forSize.call(this, width, height), score = _a.score, relativeBox = _a.relativeBox, imageDims = _a.imageDims;
return new FaceDetection(score, relativeBox, imageDims);
};
return FaceDetection;
}(tfjs_image_recognition_base_1.ObjectDetection));
exports.FaceDetection = FaceDetection;
//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
build/commonjs/classes/FaceDetection.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/classes/FaceLandmarks.d.ts deleted 100644 → 0
import { Box, Dimensions, IBoundingBox, IDimensions, IRect, Point } from 'tfjs-image-recognition-base';
import { FaceDetection } from './FaceDetection';
export interface IFaceLandmarks {
positions: Point[];
shift: Point;
}
export declare class FaceLandmarks implements IFaceLandmarks {
protected _shift: Point;
protected _positions: Point[];
protected _imgDims: Dimensions;
constructor(relativeFaceLandmarkPositions: Point[], imgDims: IDimensions, shift?: Point);
readonly shift: Point;
readonly imageWidth: number;
readonly imageHeight: number;
readonly positions: Point[];
readonly relativePositions: Point[];
forSize<T extends FaceLandmarks>(width: number, height: number): T;
shiftBy<T extends FaceLandmarks>(x: number, y: number): T;
shiftByPoint<T extends FaceLandmarks>(pt: Point): T;
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
align(detection?: FaceDetection | IRect | IBoundingBox | null, options?: {
useDlibAlignment?: boolean;
minBoxPadding?: number;
}): Box;
private alignDlib;
private alignMinBbox;
protected getRefPointsForAlignment(): Point[];
}
build/commonjs/classes/FaceLandmarks.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var minBbox_1 = require("../minBbox");
var FaceDetection_1 = require("./FaceDetection");
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imgDims, shift) {
if (shift === void 0) { shift = new tfjs_image_recognition_base_1.Point(0, 0); }
var width = imgDims.width, height = imgDims.height;
this._imgDims = new tfjs_image_recognition_base_1.Dimensions(width, height);
this._shift = shift;
this._positions = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new tfjs_image_recognition_base_1.Point(width, height)).add(shift); });
}
Object.defineProperty(FaceLandmarks.prototype, "shift", {
get: function () { return new tfjs_image_recognition_base_1.Point(this._shift.x, this._shift.y); },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceLandmarks.prototype, "imageWidth", {
get: function () { return this._imgDims.width; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceLandmarks.prototype, "imageHeight", {
get: function () { return this._imgDims.height; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceLandmarks.prototype, "positions", {
get: function () { return this._positions; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceLandmarks.prototype, "relativePositions", {
get: function () {
var _this = this;
return this._positions.map(function (pt) { return pt.sub(_this._shift).div(new tfjs_image_recognition_base_1.Point(_this.imageWidth, _this.imageHeight)); });
},
enumerable: true,
configurable: true
});
FaceLandmarks.prototype.forSize = function (width, height) {
return new this.constructor(this.relativePositions, { width: width, height: height });
};
FaceLandmarks.prototype.shiftBy = function (x, y) {
return new this.constructor(this.relativePositions, this._imgDims, new tfjs_image_recognition_base_1.Point(x, y));
};
FaceLandmarks.prototype.shiftByPoint = function (pt) {
return this.shiftBy(pt.x, pt.y);
};
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
FaceLandmarks.prototype.align = function (detection, options) {
if (options === void 0) { options = {}; }
if (detection) {
var box = detection instanceof FaceDetection_1.FaceDetection
? detection.box.floor()
: new tfjs_image_recognition_base_1.Box(detection);
return this.shiftBy(box.x, box.y).align(null, options);
}
var _a = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options), useDlibAlignment = _a.useDlibAlignment, minBoxPadding = _a.minBoxPadding;
if (useDlibAlignment) {
return this.alignDlib();
}
return this.alignMinBbox(minBoxPadding);
};
FaceLandmarks.prototype.alignDlib = function () {
var centers = this.getRefPointsForAlignment();
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = tfjs_image_recognition_base_1.getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new tfjs_image_recognition_base_1.Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
};
FaceLandmarks.prototype.alignMinBbox = function (padding) {
var box = minBbox_1.minBbox(this.positions);
return box.pad(box.width * padding, box.height * padding);
};
FaceLandmarks.prototype.getRefPointsForAlignment = function () {
throw new Error('getRefPointsForAlignment not implemented by base class');
};
return FaceLandmarks;
}());
exports.FaceLandmarks = FaceLandmarks;
//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
build/commonjs/classes/FaceLandmarks.js.map deleted 100644 → 0
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build/commonjs/classes/FaceLandmarks5.d.ts deleted 100644 → 0
import { Point } from 'tfjs-image-recognition-base';
import { FaceLandmarks } from './FaceLandmarks';
export declare class FaceLandmarks5 extends FaceLandmarks {
protected getRefPointsForAlignment(): Point[];
}
build/commonjs/classes/FaceLandmarks5.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceLandmarks_1 = require("./FaceLandmarks");
var FaceLandmarks5 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks5, _super);
function FaceLandmarks5() {
return _super !== null && _super.apply(this, arguments) || this;
}
FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
var pts = this.positions;
return [
pts[0],
pts[1],
tfjs_image_recognition_base_1.getCenterPoint([pts[3], pts[4]])
];
};
return FaceLandmarks5;
}(FaceLandmarks_1.FaceLandmarks));
exports.FaceLandmarks5 = FaceLandmarks5;
//# sourceMappingURL=FaceLandmarks5.js.map
\ No newline at end of file
build/commonjs/classes/FaceLandmarks5.js.map deleted 100644 → 0
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build/commonjs/classes/FaceLandmarks68.d.ts deleted 100644 → 0
import { Point } from 'tfjs-image-recognition-base';
import { FaceLandmarks } from '../classes/FaceLandmarks';
export declare class FaceLandmarks68 extends FaceLandmarks {
getJawOutline(): Point[];
getLeftEyeBrow(): Point[];
getRightEyeBrow(): Point[];
getNose(): Point[];
getLeftEye(): Point[];
getRightEye(): Point[];
getMouth(): Point[];
protected getRefPointsForAlignment(): Point[];
}
build/commonjs/classes/FaceLandmarks68.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceLandmarks_1 = require("../classes/FaceLandmarks");
var FaceLandmarks68 = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarks68, _super);
function FaceLandmarks68() {
return _super !== null && _super.apply(this, arguments) || this;
}
FaceLandmarks68.prototype.getJawOutline = function () {
return this.positions.slice(0, 17);
};
FaceLandmarks68.prototype.getLeftEyeBrow = function () {
return this.positions.slice(17, 22);
};
FaceLandmarks68.prototype.getRightEyeBrow = function () {
return this.positions.slice(22, 27);
};
FaceLandmarks68.prototype.getNose = function () {
return this.positions.slice(27, 36);
};
FaceLandmarks68.prototype.getLeftEye = function () {
return this.positions.slice(36, 42);
};
FaceLandmarks68.prototype.getRightEye = function () {
return this.positions.slice(42, 48);
};
FaceLandmarks68.prototype.getMouth = function () {
return this.positions.slice(48, 68);
};
FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
return [
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(tfjs_image_recognition_base_1.getCenterPoint);
};
return FaceLandmarks68;
}(FaceLandmarks_1.FaceLandmarks));
exports.FaceLandmarks68 = FaceLandmarks68;
//# sourceMappingURL=FaceLandmarks68.js.map
\ No newline at end of file
build/commonjs/classes/FaceLandmarks68.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/classes/FaceMatch.d.ts deleted 100644 → 0
export interface IFaceMatch {
label: string;
distance: number;
}
export declare class FaceMatch implements IFaceMatch {
private _label;
private _distance;
constructor(label: string, distance: number);
readonly label: string;
readonly distance: number;
toString(withDistance?: boolean): string;
}
build/commonjs/classes/FaceMatch.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceMatch = /** @class */ (function () {
function FaceMatch(label, distance) {
this._label = label;
this._distance = distance;
}
Object.defineProperty(FaceMatch.prototype, "label", {
get: function () { return this._label; },
enumerable: true,
configurable: true
});
Object.defineProperty(FaceMatch.prototype, "distance", {
get: function () { return this._distance; },
enumerable: true,
configurable: true
});
FaceMatch.prototype.toString = function (withDistance) {
if (withDistance === void 0) { withDistance = true; }
return "" + this.label + (withDistance ? " (" + tfjs_image_recognition_base_1.round(this.distance) + ")" : '');
};
return FaceMatch;
}());
exports.FaceMatch = FaceMatch;
//# sourceMappingURL=FaceMatch.js.map
\ No newline at end of file
build/commonjs/classes/FaceMatch.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/classes/LabeledFaceDescriptors.d.ts deleted 100644 → 0
export declare class LabeledFaceDescriptors {
private _label;
private _descriptors;
constructor(label: string, descriptors: Float32Array[]);
readonly label: string;
readonly descriptors: Float32Array[];
toJSON(): any;
static fromJSON(json: any): LabeledFaceDescriptors;
}
build/commonjs/classes/LabeledFaceDescriptors.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var LabeledFaceDescriptors = /** @class */ (function () {
function LabeledFaceDescriptors(label, descriptors) {
if (!(typeof label === 'string')) {
throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');
}
if (!Array.isArray(descriptors) || descriptors.some(function (desc) { return !(desc instanceof Float32Array); })) {
throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');
}
this._label = label;
this._descriptors = descriptors;
}
Object.defineProperty(LabeledFaceDescriptors.prototype, "label", {
get: function () { return this._label; },
enumerable: true,
configurable: true
});
Object.defineProperty(LabeledFaceDescriptors.prototype, "descriptors", {
get: function () { return this._descriptors; },
enumerable: true,
configurable: true
});
LabeledFaceDescriptors.prototype.toJSON = function () {
return {
label: this.label,
descriptors: this.descriptors.map(function (d) { return Array.from(d); })
};
};
LabeledFaceDescriptors.fromJSON = function (json) {
var descriptors = json.descriptors.map(function (d) {
return new Float32Array(d);
});
return new LabeledFaceDescriptors(json.label, descriptors);
};
return LabeledFaceDescriptors;
}());
exports.LabeledFaceDescriptors = LabeledFaceDescriptors;
//# sourceMappingURL=LabeledFaceDescriptors.js.map
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build/commonjs/classes/LabeledFaceDescriptors.js.map deleted 100644 → 0
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build/commonjs/classes/index.d.ts deleted 100644 → 0
export * from './FaceDetection';
export * from './FaceLandmarks';
export * from './FaceLandmarks5';
export * from './FaceLandmarks68';
export * from './FaceMatch';
export * from './LabeledFaceDescriptors';
build/commonjs/classes/index.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./FaceDetection"), exports);
tslib_1.__exportStar(require("./FaceLandmarks"), exports);
tslib_1.__exportStar(require("./FaceLandmarks5"), exports);
tslib_1.__exportStar(require("./FaceLandmarks68"), exports);
tslib_1.__exportStar(require("./FaceMatch"), exports);
tslib_1.__exportStar(require("./LabeledFaceDescriptors"), exports);
//# sourceMappingURL=index.js.map
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build/commonjs/classes/index.js.map deleted 100644 → 0
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build/commonjs/common/depthwiseSeparableConv.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
export declare function depthwiseSeparableConv(x: tf.Tensor4D, params: TfjsImageRecognitionBase.SeparableConvParams, stride: [number, number]): tf.Tensor4D;
build/commonjs/common/depthwiseSeparableConv.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function depthwiseSeparableConv(x, params, stride) {
return tf.tidy(function () {
var out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
out = tf.add(out, params.bias);
return out;
});
}
exports.depthwiseSeparableConv = depthwiseSeparableConv;
//# sourceMappingURL=depthwiseSeparableConv.js.map
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build/commonjs/common/depthwiseSeparableConv.js.map deleted 100644 → 0
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build/commonjs/common/fullyConnectedLayer.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
export declare function fullyConnectedLayer(x: tf.Tensor2D, params: TfjsImageRecognitionBase.FCParams): tf.Tensor2D;
build/commonjs/common/fullyConnectedLayer.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function fullyConnectedLayer(x, params) {
return tf.tidy(function () {
return tf.add(tf.matMul(x, params.weights), params.bias);
});
}
exports.fullyConnectedLayer = fullyConnectedLayer;
//# sourceMappingURL=fullyConnectedLayer.js.map
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build/commonjs/common/fullyConnectedLayer.js.map deleted 100644 → 0
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build/commonjs/common/loadConvParamsFactory.d.ts deleted 100644 → 0
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
export declare function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T): (prefix: string) => TfjsImageRecognitionBase.ConvParams;
build/commonjs/common/loadConvParamsFactory.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function loadConvParamsFactory(extractWeightEntry) {
return function (prefix) {
var filters = extractWeightEntry(prefix + "/filters", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return { filters: filters, bias: bias };
};
}
exports.loadConvParamsFactory = loadConvParamsFactory;
//# sourceMappingURL=loadConvParamsFactory.js.map
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build/commonjs/common/loadConvParamsFactory.js.map deleted 100644 → 0
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build/commonjs/dom/extractFaceTensors.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { Rect } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images.
* Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually.
*
* @param imageTensor The image tensor that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export declare function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor3D[]>;
build/commonjs/dom/extractFaceTensors.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
/**
* Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images.
* Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually.
*
* @param imageTensor The image tensor that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
function extractFaceTensors(imageTensor, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
return tslib_1.__generator(this, function (_a) {
if (!tfjs_image_recognition_base_1.isTensor3D(imageTensor) && !tfjs_image_recognition_base_1.isTensor4D(imageTensor)) {
throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');
}
if (tfjs_image_recognition_base_1.isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
return [2 /*return*/, tf.tidy(function () {
var _a = imageTensor.shape.slice(tfjs_image_recognition_base_1.isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).box
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
});
return faceTensors;
})];
});
});
}
exports.extractFaceTensors = extractFaceTensors;
//# sourceMappingURL=extractFaceTensors.js.map
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build/commonjs/dom/extractFaceTensors.js.map deleted 100644 → 0
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build/commonjs/dom/extractFaces.d.ts deleted 100644 → 0
import { Rect, TNetInput } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the image regions containing the detected faces.
*
* @param input The image that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
export declare function extractFaces(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<HTMLCanvasElement[]>;
build/commonjs/dom/extractFaces.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
/**
* Extracts the image regions containing the detected faces.
*
* @param input The image that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
function extractFaces(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var Canvas, canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
Canvas = tfjs_image_recognition_base_1.env.getEnv().Canvas;
canvas = input;
if (!!(input instanceof Canvas)) return [3 /*break*/, 5];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _b.sent();
if (netInput.batchSize > 1) {
throw new Error('extractFaces - batchSize > 1 not supported');
}
tensorOrCanvas = netInput.getInput(0);
if (!(tensorOrCanvas instanceof Canvas)) return [3 /*break*/, 2];
_a = tensorOrCanvas;
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, tfjs_image_recognition_base_1.imageTensorToCanvas(tensorOrCanvas)];
case 3:
_a = _b.sent();
_b.label = 4;
case 4:
canvas = _a;
_b.label = 5;
case 5:
ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(canvas.width, canvas.height).box.floor()
: det; })
.map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
return [2 /*return*/, boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = tfjs_image_recognition_base_1.createCanvas({ width: width, height: height });
tfjs_image_recognition_base_1.getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg;
})];
}
});
});
}
exports.extractFaces = extractFaces;
//# sourceMappingURL=extractFaces.js.map
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build/commonjs/dom/extractFaces.js.map deleted 100644 → 0
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build/commonjs/dom/index.d.ts deleted 100644 → 0
export * from './extractFaces';
export * from './extractFaceTensors';
build/commonjs/dom/index.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./extractFaces"), exports);
tslib_1.__exportStar(require("./extractFaceTensors"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/dom/index.js.map deleted 100644 → 0
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build/commonjs/draw/DrawFaceLandmarks.d.ts deleted 100644 → 0
import { FaceLandmarks } from '../classes/FaceLandmarks';
import { WithFaceDetection } from '../factories/WithFaceDetection';
import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
export interface IDrawFaceLandmarksOptions {
drawLines?: boolean;
drawPoints?: boolean;
lineWidth?: number;
pointSize?: number;
lineColor?: string;
pointColor?: string;
}
export declare class DrawFaceLandmarksOptions {
drawLines: boolean;
drawPoints: boolean;
lineWidth: number;
pointSize: number;
lineColor: string;
pointColor: string;
constructor(options?: IDrawFaceLandmarksOptions);
}
export declare class DrawFaceLandmarks {
faceLandmarks: FaceLandmarks;
options: DrawFaceLandmarksOptions;
constructor(faceLandmarks: FaceLandmarks, options?: IDrawFaceLandmarksOptions);
draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void;
}
export declare type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>;
export declare function drawFaceLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>): void;
build/commonjs/draw/DrawFaceLandmarks.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceLandmarks_1 = require("../classes/FaceLandmarks");
var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
var WithFaceLandmarks_1 = require("../factories/WithFaceLandmarks");
var drawContour_1 = require("./drawContour");
var DrawFaceLandmarksOptions = /** @class */ (function () {
function DrawFaceLandmarksOptions(options) {
if (options === void 0) { options = {}; }
var _a = options.drawLines, drawLines = _a === void 0 ? true : _a, _b = options.drawPoints, drawPoints = _b === void 0 ? true : _b, lineWidth = options.lineWidth, lineColor = options.lineColor, pointSize = options.pointSize, pointColor = options.pointColor;
this.drawLines = drawLines;
this.drawPoints = drawPoints;
this.lineWidth = lineWidth || 1;
this.pointSize = pointSize || 2;
this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';
this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';
}
return DrawFaceLandmarksOptions;
}());
exports.DrawFaceLandmarksOptions = DrawFaceLandmarksOptions;
var DrawFaceLandmarks = /** @class */ (function () {
function DrawFaceLandmarks(faceLandmarks, options) {
if (options === void 0) { options = {}; }
this.faceLandmarks = faceLandmarks;
this.options = new DrawFaceLandmarksOptions(options);
}
DrawFaceLandmarks.prototype.draw = function (canvasArg) {
var ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvasArg);
var _a = this.options, drawLines = _a.drawLines, drawPoints = _a.drawPoints, lineWidth = _a.lineWidth, lineColor = _a.lineColor, pointSize = _a.pointSize, pointColor = _a.pointColor;
if (drawLines && this.faceLandmarks instanceof FaceLandmarks68_1.FaceLandmarks68) {
ctx.strokeStyle = lineColor;
ctx.lineWidth = lineWidth;
drawContour_1.drawContour(ctx, this.faceLandmarks.getJawOutline());
drawContour_1.drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());
drawContour_1.drawContour(ctx, this.faceLandmarks.getRightEyeBrow());
drawContour_1.drawContour(ctx, this.faceLandmarks.getNose());
drawContour_1.drawContour(ctx, this.faceLandmarks.getLeftEye(), true);
drawContour_1.drawContour(ctx, this.faceLandmarks.getRightEye(), true);
drawContour_1.drawContour(ctx, this.faceLandmarks.getMouth(), true);
}
if (drawPoints) {
ctx.strokeStyle = pointColor;
ctx.fillStyle = pointColor;
var drawPoint = function (pt) {
ctx.beginPath();
ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);
ctx.fill();
};
this.faceLandmarks.positions.forEach(drawPoint);
}
};
return DrawFaceLandmarks;
}());
exports.DrawFaceLandmarks = DrawFaceLandmarks;
function drawFaceLandmarks(canvasArg, faceLandmarks) {
var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
faceLandmarksArray.forEach(function (f) {
var landmarks = f instanceof FaceLandmarks_1.FaceLandmarks
? f
: (WithFaceLandmarks_1.isWithFaceLandmarks(f) ? f.landmarks : undefined);
if (!landmarks) {
throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');
}
new DrawFaceLandmarks(landmarks).draw(canvasArg);
});
}
exports.drawFaceLandmarks = drawFaceLandmarks;
//# sourceMappingURL=DrawFaceLandmarks.js.map
\ No newline at end of file
build/commonjs/draw/DrawFaceLandmarks.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/draw/drawContour.d.ts deleted 100644 → 0
import { Point } from 'tfjs-image-recognition-base';
export declare function drawContour(ctx: CanvasRenderingContext2D, points: Point[], isClosed?: boolean): void;
build/commonjs/draw/drawContour.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function drawContour(ctx, points, isClosed) {
if (isClosed === void 0) { isClosed = false; }
ctx.beginPath();
points.slice(1).forEach(function (_a, prevIdx) {
var x = _a.x, y = _a.y;
var from = points[prevIdx];
ctx.moveTo(from.x, from.y);
ctx.lineTo(x, y);
});
if (isClosed) {
var from = points[points.length - 1];
var to = points[0];
if (!from || !to) {
return;
}
ctx.moveTo(from.x, from.y);
ctx.lineTo(to.x, to.y);
}
ctx.stroke();
}
exports.drawContour = drawContour;
//# sourceMappingURL=drawContour.js.map
\ No newline at end of file
build/commonjs/draw/drawContour.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/draw/drawDetections.d.ts deleted 100644 → 0
import { IBoundingBox, IRect } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
import { WithFaceDetection } from '../factories/WithFaceDetection';
export declare type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>;
export declare function drawDetections(canvasArg: string | HTMLCanvasElement, detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>): void;
build/commonjs/draw/drawDetections.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceDetection_1 = require("../classes/FaceDetection");
var WithFaceDetection_1 = require("../factories/WithFaceDetection");
function drawDetections(canvasArg, detections) {
var detectionsArray = Array.isArray(detections) ? detections : [detections];
detectionsArray.forEach(function (det) {
var score = det instanceof FaceDetection_1.FaceDetection
? det.score
: (WithFaceDetection_1.isWithFaceDetection(det) ? det.detection.score : undefined);
var box = det instanceof FaceDetection_1.FaceDetection
? det.box
: (WithFaceDetection_1.isWithFaceDetection(det) ? det.detection.box : new tfjs_image_recognition_base_1.Box(det));
var label = score ? "" + tfjs_image_recognition_base_1.round(score) : undefined;
new tfjs_image_recognition_base_1.draw.DrawBox(box, { label: label }).draw(canvasArg);
});
}
exports.drawDetections = drawDetections;
//# sourceMappingURL=drawDetections.js.map
\ No newline at end of file
build/commonjs/draw/drawDetections.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/draw/drawFaceExpressions.d.ts deleted 100644 → 0
import { IPoint } from 'tfjs-image-recognition-base';
import { FaceExpressions } from '../faceExpressionNet';
import { WithFaceExpressions } from '../factories/WithFaceExpressions';
export declare type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>;
export declare function drawFaceExpressions(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>, minConfidence?: number, textFieldAnchor?: IPoint): void;
build/commonjs/draw/drawFaceExpressions.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var faceExpressionNet_1 = require("../faceExpressionNet");
var WithFaceDetection_1 = require("../factories/WithFaceDetection");
var WithFaceExpressions_1 = require("../factories/WithFaceExpressions");
function drawFaceExpressions(canvasArg, faceExpressions, minConfidence, textFieldAnchor) {
if (minConfidence === void 0) { minConfidence = 0.1; }
var faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];
faceExpressionsArray.forEach(function (e) {
var expr = e instanceof faceExpressionNet_1.FaceExpressions
? e
: (WithFaceExpressions_1.isWithFaceExpressions(e) ? e.expressions : undefined);
if (!expr) {
throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');
}
var sorted = expr.asSortedArray();
var resultsToDisplay = sorted.filter(function (expr) { return expr.probability > minConfidence; });
var anchor = WithFaceDetection_1.isWithFaceDetection(e)
? e.detection.box.bottomLeft
: (textFieldAnchor || new tfjs_image_recognition_base_1.Point(0, 0));
var drawTextField = new tfjs_image_recognition_base_1.draw.DrawTextField(resultsToDisplay.map(function (expr) { return expr.expression + " (" + tfjs_image_recognition_base_1.round(expr.probability) + ")"; }), anchor);
drawTextField.draw(canvasArg);
});
}
exports.drawFaceExpressions = drawFaceExpressions;
//# sourceMappingURL=drawFaceExpressions.js.map
\ No newline at end of file
build/commonjs/draw/drawFaceExpressions.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/draw/index.d.ts deleted 100644 → 0
export * from './drawContour';
export * from './drawDetections';
export * from './drawFaceExpressions';
export * from './DrawFaceLandmarks';
build/commonjs/draw/index.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./drawContour"), exports);
tslib_1.__exportStar(require("./drawDetections"), exports);
tslib_1.__exportStar(require("./drawFaceExpressions"), exports);
tslib_1.__exportStar(require("./DrawFaceLandmarks"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/draw/index.js.map deleted 100644 → 0
{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/draw/index.ts"],"names":[],"mappings":";;;AAAA,wDAA6B;AAC7B,2DAAgC;AAChC,gEAAqC;AACrC,8DAAmC"}
\ No newline at end of file
build/commonjs/euclideanDistance.d.ts deleted 100644 → 0
export declare function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array): number;
build/commonjs/euclideanDistance.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function euclideanDistance(arr1, arr2) {
if (arr1.length !== arr2.length)
throw new Error('euclideanDistance: arr1.length !== arr2.length');
var desc1 = Array.from(arr1);
var desc2 = Array.from(arr2);
return Math.sqrt(desc1
.map(function (val, i) { return val - desc2[i]; })
.reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
}
exports.euclideanDistance = euclideanDistance;
//# sourceMappingURL=euclideanDistance.js.map
\ No newline at end of file
build/commonjs/euclideanDistance.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/faceExpressionNet/FaceExpressionNet.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, TNetInput } from 'tfjs-image-recognition-base';
import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
import { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
import { FaceProcessor } from '../faceProcessor/FaceProcessor';
import { FaceExpressions } from './FaceExpressions';
export declare class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {
constructor(faceFeatureExtractor?: FaceFeatureExtractor);
forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
predictExpressions(input: TNetInput): Promise<FaceExpressions | FaceExpressions[]>;
protected getDefaultModelName(): string;
protected getClassifierChannelsIn(): number;
protected getClassifierChannelsOut(): number;
}
build/commonjs/faceExpressionNet/FaceExpressionNet.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceFeatureExtractor_1 = require("../faceFeatureExtractor/FaceFeatureExtractor");
var FaceProcessor_1 = require("../faceProcessor/FaceProcessor");
var FaceExpressions_1 = require("./FaceExpressions");
var FaceExpressionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceExpressionNet, _super);
function FaceExpressionNet(faceFeatureExtractor) {
if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new FaceFeatureExtractor_1.FaceFeatureExtractor(); }
return _super.call(this, 'FaceExpressionNet', faceFeatureExtractor) || this;
}
FaceExpressionNet.prototype.forwardInput = function (input) {
var _this = this;
return tf.tidy(function () { return tf.softmax(_this.runNet(input)); });
};
FaceExpressionNet.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceExpressionNet.prototype.predictExpressions = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, out, probabilitesByBatch, predictionsByBatch;
var _this = this;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _a.sent();
return [4 /*yield*/, this.forwardInput(netInput)];
case 2:
out = _a.sent();
return [4 /*yield*/, Promise.all(tf.unstack(out).map(function (t) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
var data;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, t.data()];
case 1:
data = _a.sent();
t.dispose();
return [2 /*return*/, data];
}
});
}); }))];
case 3:
probabilitesByBatch = _a.sent();
out.dispose();
predictionsByBatch = probabilitesByBatch
.map(function (probabilites) { return new FaceExpressions_1.FaceExpressions(probabilites); });
return [2 /*return*/, netInput.isBatchInput
? predictionsByBatch
: predictionsByBatch[0]];
}
});
});
};
FaceExpressionNet.prototype.getDefaultModelName = function () {
return 'face_expression_model';
};
FaceExpressionNet.prototype.getClassifierChannelsIn = function () {
return 256;
};
FaceExpressionNet.prototype.getClassifierChannelsOut = function () {
return 7;
};
return FaceExpressionNet;
}(FaceProcessor_1.FaceProcessor));
exports.FaceExpressionNet = FaceExpressionNet;
//# sourceMappingURL=FaceExpressionNet.js.map
\ No newline at end of file
build/commonjs/faceExpressionNet/FaceExpressionNet.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/faceExpressionNet/FaceExpressions.d.ts deleted 100644 → 0
export declare const FACE_EXPRESSION_LABELS: string[];
export declare class FaceExpressions {
neutral: number;
happy: number;
sad: number;
angry: number;
fearful: number;
disgusted: number;
surprised: number;
constructor(probabilities: number[] | Float32Array);
asSortedArray(): {
expression: string;
probability: number;
}[];
}
build/commonjs/faceExpressionNet/FaceExpressions.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];
var FaceExpressions = /** @class */ (function () {
function FaceExpressions(probabilities) {
var _this = this;
if (probabilities.length !== 7) {
throw new Error("FaceExpressions.constructor - expected probabilities.length to be 7, have: " + probabilities.length);
}
exports.FACE_EXPRESSION_LABELS.forEach(function (expression, idx) {
_this[expression] = probabilities[idx];
});
}
FaceExpressions.prototype.asSortedArray = function () {
var _this = this;
return exports.FACE_EXPRESSION_LABELS
.map(function (expression) { return ({ expression: expression, probability: _this[expression] }); })
.sort(function (e0, e1) { return e1.probability - e0.probability; });
};
return FaceExpressions;
}());
exports.FaceExpressions = FaceExpressions;
//# sourceMappingURL=FaceExpressions.js.map
\ No newline at end of file
build/commonjs/faceExpressionNet/FaceExpressions.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/faceExpressionNet/index.d.ts deleted 100644 → 0
export * from './FaceExpressionNet';
export * from './FaceExpressions';
build/commonjs/faceExpressionNet/index.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
tslib_1.__exportStar(require("./FaceExpressionNet"), exports);
tslib_1.__exportStar(require("./FaceExpressions"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/commonjs/faceExpressionNet/index.js.map deleted 100644 → 0
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\ No newline at end of file
build/commonjs/faceFeatureExtractor/FaceFeatureExtractor.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';
export declare class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {
constructor();
forwardInput(input: NetInput): tf.Tensor4D;
forward(input: TNetInput): Promise<tf.Tensor4D>;
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: FaceFeatureExtractorParams;
paramMappings: import("tfjs-image-recognition-base/build/commonjs/common").ParamMapping[];
};
protected extractParams(weights: Float32Array): {
params: FaceFeatureExtractorParams;
paramMappings: import("tfjs-image-recognition-base/build/commonjs/common").ParamMapping[];
};
}
build/commonjs/faceFeatureExtractor/FaceFeatureExtractor.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var denseBlock_1 = require("./denseBlock");
var extractParams_1 = require("./extractParams");
var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
var FaceFeatureExtractor = /** @class */ (function (_super) {
tslib_1.__extends(FaceFeatureExtractor, _super);
function FaceFeatureExtractor() {
return _super.call(this, 'FaceFeatureExtractor') || this;
}
FaceFeatureExtractor.prototype.forwardInput = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceFeatureExtractor - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(112, true);
var meanRgb = [122.782, 117.001, 104.298];
var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(255));
var out = denseBlock_1.denseBlock4(normalized, params.dense0, true);
out = denseBlock_1.denseBlock4(out, params.dense1);
out = denseBlock_1.denseBlock4(out, params.dense2);
out = denseBlock_1.denseBlock4(out, params.dense3);
out = tf.avgPool(out, [7, 7], [2, 2], 'valid');
return out;
});
};
FaceFeatureExtractor.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceFeatureExtractor.prototype.getDefaultModelName = function () {
return 'face_feature_extractor_model';
};
FaceFeatureExtractor.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(weightMap);
};
FaceFeatureExtractor.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return FaceFeatureExtractor;
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.FaceFeatureExtractor = FaceFeatureExtractor;
//# sourceMappingURL=FaceFeatureExtractor.js.map
\ No newline at end of file
build/commonjs/faceFeatureExtractor/FaceFeatureExtractor.js.map deleted 100644 → 0
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build/commonjs/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
import { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';
export declare class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {
constructor();
forwardInput(input: NetInput): tf.Tensor4D;
forward(input: TNetInput): Promise<tf.Tensor4D>;
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: TinyFaceFeatureExtractorParams;
paramMappings: import("tfjs-image-recognition-base/build/commonjs/common").ParamMapping[];
};
protected extractParams(weights: Float32Array): {
params: TinyFaceFeatureExtractorParams;
paramMappings: import("tfjs-image-recognition-base/build/commonjs/common").ParamMapping[];
};
}
build/commonjs/faceFeatureExtractor/TinyFaceFeatureExtractor.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var denseBlock_1 = require("./denseBlock");
var extractParamsFromWeigthMapTiny_1 = require("./extractParamsFromWeigthMapTiny");
var extractParamsTiny_1 = require("./extractParamsTiny");
var TinyFaceFeatureExtractor = /** @class */ (function (_super) {
tslib_1.__extends(TinyFaceFeatureExtractor, _super);
function TinyFaceFeatureExtractor() {
return _super.call(this, 'TinyFaceFeatureExtractor') || this;
}
TinyFaceFeatureExtractor.prototype.forwardInput = function (input) {
var params = this.params;
if (!params) {
throw new Error('TinyFaceFeatureExtractor - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(112, true);
var meanRgb = [122.782, 117.001, 104.298];
var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(255));
var out = denseBlock_1.denseBlock3(normalized, params.dense0, true);
out = denseBlock_1.denseBlock3(out, params.dense1);
out = denseBlock_1.denseBlock3(out, params.dense2);
out = tf.avgPool(out, [14, 14], [2, 2], 'valid');
return out;
});
};
TinyFaceFeatureExtractor.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
TinyFaceFeatureExtractor.prototype.getDefaultModelName = function () {
return 'face_feature_extractor_tiny_model';
};
TinyFaceFeatureExtractor.prototype.extractParamsFromWeigthMap = function (weightMap) {
return extractParamsFromWeigthMapTiny_1.extractParamsFromWeigthMapTiny(weightMap);
};
TinyFaceFeatureExtractor.prototype.extractParams = function (weights) {
return extractParamsTiny_1.extractParamsTiny(weights);
};
return TinyFaceFeatureExtractor;
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.TinyFaceFeatureExtractor = TinyFaceFeatureExtractor;
//# sourceMappingURL=TinyFaceFeatureExtractor.js.map
\ No newline at end of file
build/commonjs/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map deleted 100644 → 0
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build/commonjs/faceFeatureExtractor/denseBlock.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { DenseBlock3Params, DenseBlock4Params } from './types';
export declare function denseBlock3(x: tf.Tensor4D, denseBlockParams: DenseBlock3Params, isFirstLayer?: boolean): tf.Tensor4D;
export declare function denseBlock4(x: tf.Tensor4D, denseBlockParams: DenseBlock4Params, isFirstLayer?: boolean, isScaleDown?: boolean): tf.Tensor4D;
build/commonjs/faceFeatureExtractor/denseBlock.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var depthwiseSeparableConv_1 = require("../common/depthwiseSeparableConv");
function denseBlock3(x, denseBlockParams, isFirstLayer) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
return tf.tidy(function () {
var out1 = tf.relu(isFirstLayer
? tf.add(tf.conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
: depthwiseSeparableConv_1.depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
var out2 = depthwiseSeparableConv_1.depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
var in3 = tf.relu(tf.add(out1, out2));
var out3 = depthwiseSeparableConv_1.depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
return tf.relu(tf.add(out1, tf.add(out2, out3)));
});
}
exports.denseBlock3 = denseBlock3;
function denseBlock4(x, denseBlockParams, isFirstLayer, isScaleDown) {
if (isFirstLayer === void 0) { isFirstLayer = false; }
if (isScaleDown === void 0) { isScaleDown = true; }
return tf.tidy(function () {
var out1 = tf.relu(isFirstLayer
? tf.add(tf.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], 'same'), denseBlockParams.conv0.bias)
: depthwiseSeparableConv_1.depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
var out2 = depthwiseSeparableConv_1.depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
var in3 = tf.relu(tf.add(out1, out2));
var out3 = depthwiseSeparableConv_1.depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
var in4 = tf.relu(tf.add(out1, tf.add(out2, out3)));
var out4 = depthwiseSeparableConv_1.depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4))));
});
}
exports.denseBlock4 = denseBlock4;
//# sourceMappingURL=denseBlock.js.map
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build/commonjs/faceFeatureExtractor/denseBlock.js.map deleted 100644 → 0
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build/commonjs/faceFeatureExtractor/extractParams.d.ts deleted 100644 → 0
import { FaceFeatureExtractorParams } from './types';
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
export declare function extractParams(weights: Float32Array): {
params: FaceFeatureExtractorParams;
paramMappings: TfjsImageRecognitionBase.ParamMapping[];
};
build/commonjs/faceFeatureExtractor/extractParams.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var extractorsFactory_1 = require("./extractorsFactory");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
function extractParams(weights) {
var paramMappings = [];
var _a = tfjs_image_recognition_base_1.TfjsImageRecognitionBase.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractDenseBlock4Params = extractorsFactory_1.extractorsFactory(extractWeights, paramMappings).extractDenseBlock4Params;
var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);
var dense1 = extractDenseBlock4Params(32, 64, 'dense1');
var dense2 = extractDenseBlock4Params(64, 128, 'dense2');
var dense3 = extractDenseBlock4Params(128, 256, 'dense3');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
paramMappings: paramMappings,
params: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3 }
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/commonjs/faceFeatureExtractor/extractParams.js.map deleted 100644 → 0
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build/commonjs/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
import { FaceFeatureExtractorParams } from './types';
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: FaceFeatureExtractorParams;
paramMappings: TfjsImageRecognitionBase.ParamMapping[];
};
build/commonjs/faceFeatureExtractor/extractParamsFromWeigthMap.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var loadParamsFactory_1 = require("./loadParamsFactory");
function extractParamsFromWeigthMap(weightMap) {
var paramMappings = [];
var extractDenseBlock4Params = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings).extractDenseBlock4Params;
var params = {
dense0: extractDenseBlock4Params('dense0', true),
dense1: extractDenseBlock4Params('dense1'),
dense2: extractDenseBlock4Params('dense2'),
dense3: extractDenseBlock4Params('dense3')
};
tfjs_image_recognition_base_1.TfjsImageRecognitionBase.disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
exports.extractParamsFromWeigthMap = extractParamsFromWeigthMap;
//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
build/commonjs/faceFeatureExtractor/extractParamsFromWeigthMap.js.map deleted 100644 → 0
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build/commonjs/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
import { TfjsImageRecognitionBase } from 'tfjs-image-recognition-base';
import { TinyFaceFeatureExtractorParams } from './types';
export declare function extractParamsFromWeigthMapTiny(weightMap: tf.NamedTensorMap): {
params: TinyFaceFeatureExtractorParams;
paramMappings: TfjsImageRecognitionBase.ParamMapping[];
};
build/commonjs/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js deleted 100644 → 0
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var loadParamsFactory_1 = require("./loadParamsFactory");
function extractParamsFromWeigthMapTiny(weightMap) {
var paramMappings = [];
var extractDenseBlock3Params = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings).extractDenseBlock3Params;
var params = {
dense0: extractDenseBlock3Params('dense0', true),
dense1: extractDenseBlock3Params('dense1'),
dense2: extractDenseBlock3Params('dense2')
};
tfjs_image_recognition_base_1.TfjsImageRecognitionBase.disposeUnusedWeightTensors(weightMap, paramMappings);
return { params: params, paramMappings: paramMappings };
}
exports.extractParamsFromWeigthMapTiny = extractParamsFromWeigthMapTiny;
//# sourceMappingURL=extractParamsFromWeigthMapTiny.js.map
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build/commonjs/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js.map deleted 100644 → 0
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