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Commit 0381e197 by vincent
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check in latest build

parent 5fefdcd5
Showing with 312 additions and 122 deletions
build/BoundingBox.d.ts
import { Rect } from './Rect';
import { Dimensions } from './types';
export declare class BoundingBox {
private _left;
private _top;
......@@ -11,6 +12,7 @@ export declare class BoundingBox {
readonly bottom: number;
readonly width: number;
readonly height: number;
readonly area: number;
toSquare(): BoundingBox;
round(): BoundingBox;
padAtBorders(imageHeight: number, imageWidth: number): {
......@@ -26,5 +28,6 @@ export declare class BoundingBox {
h: number;
};
calibrate(region: BoundingBox): BoundingBox;
rescale(s: Dimensions | number): BoundingBox;
toRect(): Rect;
}
build/BoundingBox.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("./Rect");
var utils_1 = require("./utils");
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
......@@ -50,6 +51,13 @@ var BoundingBox = /** @class */ (function () {
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "area", {
get: function () {
return this.width * this.height;
},
enumerable: true,
configurable: true
});
BoundingBox.prototype.toSquare = function () {
var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
var off = (Math.abs(this.width - this.height) / 2);
......@@ -98,6 +106,11 @@ var BoundingBox = /** @class */ (function () {
BoundingBox.prototype.calibrate = function (region) {
return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
};
BoundingBox.prototype.rescale = function (s) {
var scaleX = utils_1.isDimensions(s) ? s.width : s;
var scaleY = utils_1.isDimensions(s) ? s.height : s;
return new BoundingBox(this.left * scaleX, this.top * scaleY, this.right * scaleX, this.bottom * scaleY);
};
BoundingBox.prototype.toRect = function () {
return new Rect_1.Rect(this.left, this.top, this.width, this.height);
};
......
build/BoundingBox.js.map
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build/NetInput.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { Point } from './Point';
import { TResolvedNetInput } from './types';
import { TResolvedNetInput, Dimensions } from './types';
export declare class NetInput {
private _inputs;
private _canvases;
......@@ -8,6 +8,7 @@ export declare class NetInput {
private _isBatchInput;
private _inputDimensions;
private _paddings;
private _inputSize;
constructor(inputs: tf.Tensor4D | Array<TResolvedNetInput>, isBatchInput?: boolean, keepCanvases?: boolean);
readonly inputs: tf.Tensor3D[];
readonly canvases: HTMLCanvasElement[];
......@@ -16,13 +17,18 @@ export declare class NetInput {
readonly batchSize: number;
readonly inputDimensions: number[][];
readonly paddings: Point[];
readonly inputSize: number;
readonly relativePaddings: Point[];
readonly reshapedInputDimensions: Dimensions[];
getInputDimensions(batchIdx: number): number[];
getInputHeight(batchIdx: number): number;
getInputWidth(batchIdx: number): number;
getPaddings(batchIdx: number): Point;
getRelativePaddings(batchIdx: number): Point;
getReshapedInputDimensions(batchIdx: number): Dimensions;
toBatchTensor(inputSize: number, isCenterInputs?: boolean): tf.Tensor4D;
/**
* By setting the isManaged flag, all newly created tensors will be automatically
* By setting the isManaged flag, all newly created tensors will be
* automatically disposed after the batch tensor has been created
*/
managed(): this;
......
build/NetInput.js
......@@ -16,6 +16,7 @@ var NetInput = /** @class */ (function () {
this._isBatchInput = false;
this._inputDimensions = [];
this._paddings = [];
this._inputSize = 0;
if (isTensor_1.isTensor4D(inputs)) {
this._inputs = tf.unstack(inputs);
}
......@@ -92,6 +93,29 @@ var NetInput = /** @class */ (function () {
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "inputSize", {
get: function () {
return this._inputSize;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "relativePaddings", {
get: function () {
var _this = this;
return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getRelativePaddings(batchIdx); });
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
get: function () {
var _this = this;
return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
},
enumerable: true,
configurable: true
});
NetInput.prototype.getInputDimensions = function (batchIdx) {
return this._inputDimensions[batchIdx];
};
......@@ -104,9 +128,21 @@ var NetInput = /** @class */ (function () {
NetInput.prototype.getPaddings = function (batchIdx) {
return this._paddings[batchIdx];
};
NetInput.prototype.getRelativePaddings = function (batchIdx) {
return new Point_1.Point((this.getPaddings(batchIdx).x + this.getInputWidth(batchIdx)) / this.getInputWidth(batchIdx), (this.getPaddings(batchIdx).y + this.getInputHeight(batchIdx)) / this.getInputHeight(batchIdx));
};
NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
var _a = [this.getInputHeight(batchIdx), this.getInputWidth(batchIdx)], h = _a[0], w = _a[1];
var f = this.inputSize / Math.max(h, w);
return {
height: Math.floor(h * f),
width: Math.floor(w * f)
};
};
NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
var _this = this;
if (isCenterInputs === void 0) { isCenterInputs = true; }
this._inputSize = inputSize;
return tf.tidy(function () {
var inputTensors = _this._inputs.map(function (inputTensor) {
var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1];
......@@ -127,7 +163,7 @@ var NetInput = /** @class */ (function () {
});
};
/**
* By setting the isManaged flag, all newly created tensors will be automatically
* By setting the isManaged flag, all newly created tensors will be
* automatically disposed after the batch tensor has been created
*/
NetInput.prototype.managed = function () {
......
build/NetInput.js.map
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build/Rect.d.ts
import { BoundingBox } from './BoundingBox';
export interface IRect {
x: number;
y: number;
......@@ -15,5 +16,6 @@ export declare class Rect implements IRect {
toSquare(): Rect;
pad(padX: number, padY: number): Rect;
floor(): Rect;
toBoundingBox(): BoundingBox;
clipAtImageBorders(imgWidth: number, imgHeight: number): Rect;
}
build/Rect.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var BoundingBox_1 = require("./BoundingBox");
var Rect = /** @class */ (function () {
function Rect(x, y, width, height) {
this.x = x;
......@@ -41,6 +42,9 @@ var Rect = /** @class */ (function () {
Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
};
Rect.prototype.toBoundingBox = function () {
return new BoundingBox_1.BoundingBox(this.x, this.y, this.x + this.width, this.y + this.height);
};
Rect.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
var clippedX = Math.max(x, 0);
......
build/Rect.js.map
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build/allFacesFactory.d.ts
import { TinyYolov2 } from '.';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { FullFaceDescription } from './FullFaceDescription';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams } from './mtcnn/types';
import { TinyYolov2ForwardParams } from './tinyYolov2/types';
import { TNetInput } from './types';
export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
......@@ -30,16 +30,15 @@ function computeDescriptorsFactory(recognitionNet) {
});
};
}
function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
return function (input, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
case 0: return [4 /*yield*/, detectFaces(input)];
case 1:
detections = _b.sent();
return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
......@@ -69,7 +68,36 @@ function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
});
};
}
exports.allFacesFactory = allFacesFactory;
function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectFaces, allFaces;
return tslib_1.__generator(this, function (_a) {
detectFaces = function (input) { return ssdMobilenetv1.locateFaces(input, minConfidence); };
allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
return [2 /*return*/, allFaces(input, useBatchProcessing)];
});
});
};
}
exports.allFacesSsdMobilenetv1Factory = allFacesSsdMobilenetv1Factory;
function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
return function (input, forwardParams, useBatchProcessing) {
if (forwardParams === void 0) { forwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detectFaces, allFaces;
return tslib_1.__generator(this, function (_a) {
detectFaces = function (input) { return tinyYolov2.locateFaces(input, forwardParams); };
allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
return [2 /*return*/, allFaces(input, useBatchProcessing)];
});
});
};
}
exports.allFacesTinyYolov2Factory = allFacesTinyYolov2Factory;
function allFacesMtcnnFactory(mtcnn, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) {
......
build/allFacesFactory.js.map
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\ No newline at end of file
build/commons/nonMaxSuppression.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var iou_1 = require("../iou");
function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {
if (isIOU === void 0) { isIOU = true; }
var areas = boxes.map(function (box) { return (box.width + 1) * (box.height + 1); });
var indicesSortedByScore = scores
.map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
.sort(function (c1, c2) { return c1.score - c2.score; })
......@@ -17,13 +17,7 @@ function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {
var idx = indices[i];
var currBox = boxes[curr];
var idxBox = boxes[idx];
var width = Math.max(0.0, Math.min(currBox.right, idxBox.right) - Math.max(currBox.left, idxBox.left) + 1);
var height = Math.max(0.0, Math.min(currBox.bottom, idxBox.bottom) - Math.max(currBox.top, idxBox.top) + 1);
var interSection = width * height;
var out = isIOU
? interSection / (areas[curr] + areas[idx] - interSection)
: interSection / Math.min(areas[curr], areas[idx]);
outputs.push(out);
outputs.push(iou_1.iou(currBox, idxBox, isIOU));
}
indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
};
......
build/commons/nonMaxSuppression.js.map
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\ No newline at end of file
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\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.js
......@@ -47,7 +47,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddedHeightRelative, paddedWidthRelative, results;
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
......@@ -66,18 +66,17 @@ var FaceDetectionNet = /** @class */ (function (_super) {
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
paddings = netInput.getRelativePaddings(0);
results = indices
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * paddedHeightRelative; }), top = _a[0], bottom = _a[1];
].map(function (val) { return val * paddings.y; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1];
return new FaceDetection_1.FaceDetection(scoresData[idx], new Rect_1.Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
......
build/faceDetectionNet/FaceDetectionNet.js.map
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\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.js
......@@ -3,11 +3,11 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var NeuralNetwork_1 = require("../commons/NeuralNetwork");
var normalize_1 = require("../commons/normalize");
var toNetInput_1 = require("../toNetInput");
var convLayer_1 = require("./convLayer");
var extractParams_1 = require("./extractParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var normalize_1 = require("./normalize");
var residualLayer_1 = require("./residualLayer");
var FaceRecognitionNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceRecognitionNet, _super);
......@@ -21,7 +21,8 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize_1.normalize(batchTensor);
var meanRgb = [122.782, 117.001, 104.298];
var normalized = normalize_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
var out = convLayer_1.convDown(normalized, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residualLayer_1.residual(out, params.conv32_1);
......
build/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file
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\ No newline at end of file
build/globalApi.d.ts
......@@ -15,24 +15,28 @@ export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmarkNet;
export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: {
ssdMobilenet: FaceDetectionNet;
ssdMobilenetv1: FaceDetectionNet;
faceLandmark68Net: FaceLandmarkNet;
faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn;
tinyYolov2: TinyYolov2;
};
export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadSsdMobilenetv1Model(url: string): Promise<void>;
export declare function loadFaceLandmarkModel(url: string): Promise<void>;
export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadMtcnnModel(url: string): Promise<void>;
export declare function loadTinyYolov2Model(url: string): Promise<void>;
export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
export declare type allFacesFunction = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFaces: allFacesFunction;
export declare type allFacesSsdMobilenetv1Function = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesSsdMobilenetv1: allFacesSsdMobilenetv1Function;
export declare type allFacesTinyYolov2Function = (input: tf.Tensor | NetInput | TNetInput, forwardParams?: TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesTinyYolov2: allFacesTinyYolov2Function;
export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFacesMtcnn: allFacesMtcnnFunction;
export declare const allFaces: allFacesSsdMobilenetv1Function;
build/globalApi.js
......@@ -12,16 +12,16 @@ exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
// nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided
exports.nets = {
ssdMobilenet: exports.detectionNet,
ssdMobilenetv1: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet,
faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn(),
tinyYolov2: new TinyYolov2_1.TinyYolov2()
};
function loadFaceDetectionModel(url) {
return exports.nets.ssdMobilenet.load(url);
function loadSsdMobilenetv1Model(url) {
return exports.nets.ssdMobilenetv1.load(url);
}
exports.loadFaceDetectionModel = loadFaceDetectionModel;
exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
function loadFaceLandmarkModel(url) {
return exports.nets.faceLandmark68Net.load(url);
}
......@@ -38,9 +38,13 @@ function loadTinyYolov2Model(url) {
return exports.nets.tinyYolov2.load(url);
}
exports.loadTinyYolov2Model = loadTinyYolov2Model;
function loadFaceDetectionModel(url) {
return loadSsdMobilenetv1Model(url);
}
exports.loadFaceDetectionModel = loadFaceDetectionModel;
function loadModels(url) {
return Promise.all([
loadFaceDetectionModel(url),
loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url),
loadMtcnnModel(url),
......@@ -49,7 +53,7 @@ function loadModels(url) {
}
exports.loadModels = loadModels;
function locateFaces(input, minConfidence, maxResults) {
return exports.nets.ssdMobilenet.locateFaces(input, minConfidence, maxResults);
return exports.nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
}
exports.locateFaces = locateFaces;
function detectLandmarks(input) {
......@@ -68,6 +72,8 @@ function tinyYolov2(input, forwardParams) {
return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
}
exports.tinyYolov2 = tinyYolov2;
exports.allFaces = allFacesFactory_1.allFacesFactory(exports.nets.ssdMobilenet, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesSsdMobilenetv1 = allFacesFactory_1.allFacesSsdMobilenetv1Factory(exports.nets.ssdMobilenetv1, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesTinyYolov2 = allFacesFactory_1.allFacesTinyYolov2Factory(exports.nets.tinyYolov2, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
exports.allFaces = exports.allFacesSsdMobilenetv1;
//# sourceMappingURL=globalApi.js.map
\ No newline at end of file
build/globalApi.js.map
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\ No newline at end of file
build/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
export { tf };
export * from './BoundingBox';
export * from './FaceDetection';
export * from './FullFaceDescription';
export * from './NetInput';
......@@ -13,8 +14,10 @@ export * from './faceDetectionNet';
export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './globalApi';
export * from './iou';
export * from './mtcnn';
export * from './padToSquare';
export * from './tinyYolov2';
export * from './toNetInput';
export * from './utils';
export * from './types';
build/index.js
......@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
exports.tf = tf;
tslib_1.__exportStar(require("./BoundingBox"), exports);
tslib_1.__exportStar(require("./FaceDetection"), exports);
tslib_1.__exportStar(require("./FullFaceDescription"), exports);
tslib_1.__exportStar(require("./NetInput"), exports);
......@@ -16,6 +17,7 @@ tslib_1.__exportStar(require("./faceDetectionNet"), exports);
tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
tslib_1.__exportStar(require("./globalApi"), exports);
tslib_1.__exportStar(require("./iou"), exports);
tslib_1.__exportStar(require("./mtcnn"), exports);
tslib_1.__exportStar(require("./padToSquare"), exports);
tslib_1.__exportStar(require("./tinyYolov2"), exports);
......
build/index.js.map
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\ No newline at end of file
build/tinyYolov2/TinyYolov2.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from '../BoundingBox';
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { FaceDetection } from '../FaceDetection';
import { NetInput } from '../NetInput';
import { Point } from '../Point';
import { TNetInput } from '../types';
import { NetParams, TinyYolov2ForwardParams } from './types';
import { NetParams, PostProcessingParams, TinyYolov2ForwardParams } from './types';
export declare class TinyYolov2 extends NeuralNetwork<NetParams> {
constructor();
private _withSeparableConvs;
private _anchors;
constructor(withSeparableConvs?: boolean);
readonly withSeparableConvs: boolean;
readonly anchors: Point[];
forwardInput(input: NetInput, inputSize: number): tf.Tensor4D;
forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D>;
locateFaces(input: TNetInput, forwardParams?: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
postProcess(outputTensor: tf.Tensor4D, {scoreThreshold, paddings}: PostProcessingParams): {
box: BoundingBox;
score: number;
row: number;
col: number;
anchor: number;
}[];
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
......
build/tinyYolov2/config.d.ts
......@@ -8,3 +8,5 @@ export declare const INPUT_SIZES: {
export declare const NUM_BOXES = 5;
export declare const IOU_THRESHOLD = 0.4;
export declare const BOX_ANCHORS: Point[];
export declare const BOX_ANCHORS_SEPARABLE: Point[];
export declare const MEAN_RGB: number[];
build/tinyYolov2/config.js
......@@ -11,4 +11,12 @@ exports.BOX_ANCHORS = [
new Point_1.Point(10.246, 4.59428),
new Point_1.Point(12.6868, 11.8741)
];
exports.BOX_ANCHORS_SEPARABLE = [
new Point_1.Point(1.603231, 2.094468),
new Point_1.Point(6.041143, 7.080126),
new Point_1.Point(2.882459, 3.518061),
new Point_1.Point(4.266906, 5.178857),
new Point_1.Point(9.041765, 10.66308)
];
exports.MEAN_RGB = [117.001, 114.697, 97.404];
//# sourceMappingURL=config.js.map
\ No newline at end of file
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\ No newline at end of file
build/tinyYolov2/convWithBatchNorm.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvWithBatchNorm } from './types';
export declare function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D;
import { ConvWithBatchNorm, SeparableConvParams } from './types';
export declare function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm | SeparableConvParams): tf.Tensor4D;
build/tinyYolov2/convWithBatchNorm.js
......@@ -2,13 +2,20 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var leaky_1 = require("./leaky");
var types_1 = require("./types");
function convWithBatchNorm(x, params) {
return tf.tidy(function () {
var out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');
out = tf.sub(out, params.bn.sub);
out = tf.mul(out, params.bn.truediv);
out = tf.add(out, params.conv.bias);
if (params instanceof types_1.SeparableConvParams) {
out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
out = tf.add(out, params.bias);
}
else {
out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');
out = tf.sub(out, params.bn.sub);
out = tf.mul(out, params.bn.truediv);
out = tf.add(out, params.conv.bias);
}
return leaky_1.leaky(out);
});
}
......
build/tinyYolov2/convWithBatchNorm.js.map
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build/tinyYolov2/extractParams.d.ts
import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
export declare function extractParams(weights: Float32Array, withSeparableConvs: boolean): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/tinyYolov2/extractParams.js
......@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
var types_1 = require("./types");
function extractorsFactory(extractWeights, paramMappings) {
var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
function extractBatchNormParams(size, mappedPrefix) {
......@@ -16,24 +17,34 @@ function extractorsFactory(extractWeights, paramMappings) {
var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
return { conv: conv, bn: bn };
}
function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
var pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams
extractConvWithBatchNormParams: extractConvWithBatchNormParams,
extractSeparableConvParams: extractSeparableConvParams
};
}
function extractParams(weights) {
function extractParams(weights, withSeparableConvs) {
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var paramMappings = [];
var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams;
var conv0 = extractConvWithBatchNormParams(3, 16, 'conv0');
var conv1 = extractConvWithBatchNormParams(16, 32, 'conv1');
var conv2 = extractConvWithBatchNormParams(32, 64, 'conv2');
var conv3 = extractConvWithBatchNormParams(64, 128, 'conv3');
var conv4 = extractConvWithBatchNormParams(128, 256, 'conv4');
var conv5 = extractConvWithBatchNormParams(256, 512, 'conv5');
var conv6 = extractConvWithBatchNormParams(512, 1024, 'conv6');
var conv7 = extractConvWithBatchNormParams(1024, 1024, 'conv7');
var conv8 = extractConvParams(1024, 30, 1, 'conv8');
var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
var extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
var numAnchorEncodings = withSeparableConvs ? 5 : 6;
var conv0 = extractConvFn(3, 16, 'conv0');
var conv1 = extractConvFn(16, 32, 'conv1');
var conv2 = extractConvFn(32, 64, 'conv2');
var conv3 = extractConvFn(64, 128, 'conv3');
var conv4 = extractConvFn(128, 256, 'conv4');
var conv5 = extractConvFn(256, 512, 'conv5');
var conv6 = extractConvFn(512, 1024, 'conv6');
var conv7 = extractConvFn(1024, 1024, 'conv7');
var conv8 = extractConvParams(1024, 5 * numAnchorEncodings, 1, 'conv8');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
......
build/tinyYolov2/extractParams.js.map
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\ No newline at end of file
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\ No newline at end of file
build/tinyYolov2/getDefaultParams.d.ts
import { SizeType, TinyYolov2ForwardParams } from './types';
export declare function getDefaultParams(params: TinyYolov2ForwardParams): {
sizeType: SizeType;
inputSize: SizeType;
scoreThreshold: number;
} & TinyYolov2ForwardParams;
build/tinyYolov2/getDefaultParams.js
......@@ -3,7 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
var types_1 = require("./types");
function getDefaultParams(params) {
return Object.assign({}, {
sizeType: types_1.SizeType.MD,
inputSize: types_1.SizeType.MD,
scoreThreshold: 0.5
}, params);
}
......
build/tinyYolov2/getDefaultParams.js.map
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\ No newline at end of file
{"version":3,"file":"getDefaultParams.js","sourceRoot":"","sources":["../../src/tinyYolov2/getDefaultParams.ts"],"names":[],"mappings":";;AAAA,iCAA4D;AAE5D,0BAAiC,MAA+B;IAC9D,OAAO,MAAM,CAAC,MAAM,CAClB,EAAE,EACF;QACE,SAAS,EAAE,gBAAQ,CAAC,EAAE;QACtB,cAAc,EAAE,GAAG;KACpB,EACD,MAAM,CACP,CAAA;AACH,CAAC;AATD,4CASC"}
\ No newline at end of file
build/tinyYolov2/index.d.ts
import { TinyYolov2 } from './TinyYolov2';
export * from './TinyYolov2';
export declare function createTinyYolov2(weights: Float32Array): TinyYolov2;
export declare function createTinyYolov2(weights: Float32Array, withSeparableConvs?: boolean): TinyYolov2;
build/tinyYolov2/index.js
......@@ -3,8 +3,9 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var TinyYolov2_1 = require("./TinyYolov2");
tslib_1.__exportStar(require("./TinyYolov2"), exports);
function createTinyYolov2(weights) {
var net = new TinyYolov2_1.TinyYolov2();
function createTinyYolov2(weights, withSeparableConvs) {
if (withSeparableConvs === void 0) { withSeparableConvs = true; }
var net = new TinyYolov2_1.TinyYolov2(withSeparableConvs);
net.extractWeights(weights);
return net;
}
......
build/tinyYolov2/index.js.map
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\ No newline at end of file
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\ No newline at end of file
build/tinyYolov2/leaky.js
......@@ -3,7 +3,9 @@ Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function leaky(x) {
return tf.tidy(function () {
return tf.maximum(x, tf.mul(x, tf.scalar(0.10000000149011612)));
var min = tf.mul(x, tf.scalar(0.10000000149011612));
return tf.add(tf.relu(tf.sub(x, min)), min);
//return tf.maximum(x, min)
});
}
exports.leaky = leaky;
......
build/tinyYolov2/leaky.js.map
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\ No newline at end of file
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\ No newline at end of file
build/tinyYolov2/loadQuantizedParams.d.ts
import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
export declare function loadQuantizedParams(uri: string | undefined, withSeparableConvs: boolean): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
build/tinyYolov2/loadQuantizedParams.js
......@@ -4,7 +4,9 @@ var tslib_1 = require("tslib");
var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
var loadWeightMap_1 = require("../commons/loadWeightMap");
var types_1 = require("./types");
var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractBatchNormParams(prefix) {
......@@ -22,30 +24,38 @@ function extractorsFactory(weightMap, paramMappings) {
var bn = extractBatchNormParams(prefix + "/bn");
return { conv: conv, bn: bn };
}
function extractSeparableConvParams(prefix) {
var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams
extractConvWithBatchNormParams: extractConvWithBatchNormParams,
extractSeparableConvParams: extractSeparableConvParams
};
}
function loadQuantizedParams(uri) {
function loadQuantizedParams(uri, withSeparableConvs) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, params;
var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams, extractConvFn, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams;
_a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;
extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
params = {
conv0: extractConvWithBatchNormParams('conv0'),
conv1: extractConvWithBatchNormParams('conv1'),
conv2: extractConvWithBatchNormParams('conv2'),
conv3: extractConvWithBatchNormParams('conv3'),
conv4: extractConvWithBatchNormParams('conv4'),
conv5: extractConvWithBatchNormParams('conv5'),
conv6: extractConvWithBatchNormParams('conv6'),
conv7: extractConvWithBatchNormParams('conv7'),
conv0: extractConvFn('conv0'),
conv1: extractConvFn('conv1'),
conv2: extractConvFn('conv2'),
conv3: extractConvFn('conv3'),
conv4: extractConvFn('conv4'),
conv5: extractConvFn('conv5'),
conv6: extractConvFn('conv6'),
conv7: extractConvFn('conv7'),
conv8: extractConvParams('conv8')
};
disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
......
build/tinyYolov2/loadQuantizedParams.js.map
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\ No newline at end of file
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\ No newline at end of file
build/tinyYolov2/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
import { Point } from '../Point';
export declare type BatchNorm = {
sub: tf.Tensor1D;
truediv: tf.Tensor1D;
};
export declare class SeparableConvParams {
depthwise_filter: tf.Tensor4D;
pointwise_filter: tf.Tensor4D;
bias: tf.Tensor1D;
constructor(depthwise_filter: tf.Tensor4D, pointwise_filter: tf.Tensor4D, bias: tf.Tensor1D);
}
export declare type ConvWithBatchNorm = {
conv: ConvParams;
bn: BatchNorm;
};
export declare type NetParams = {
conv0: ConvWithBatchNorm;
conv1: ConvWithBatchNorm;
conv2: ConvWithBatchNorm;
conv3: ConvWithBatchNorm;
conv4: ConvWithBatchNorm;
conv5: ConvWithBatchNorm;
conv6: ConvWithBatchNorm;
conv7: ConvWithBatchNorm;
conv0: ConvWithBatchNorm | SeparableConvParams;
conv1: ConvWithBatchNorm | SeparableConvParams;
conv2: ConvWithBatchNorm | SeparableConvParams;
conv3: ConvWithBatchNorm | SeparableConvParams;
conv4: ConvWithBatchNorm | SeparableConvParams;
conv5: ConvWithBatchNorm | SeparableConvParams;
conv6: ConvWithBatchNorm | SeparableConvParams;
conv7: ConvWithBatchNorm | SeparableConvParams;
conv8: ConvParams;
};
export declare enum SizeType {
......@@ -29,3 +36,7 @@ export declare type TinyYolov2ForwardParams = {
inputSize?: SizeType | number;
scoreThreshold?: number;
};
export declare type PostProcessingParams = {
scoreThreshold?: number;
paddings: Point;
};
build/tinyYolov2/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var SeparableConvParams = /** @class */ (function () {
function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {
this.depthwise_filter = depthwise_filter;
this.pointwise_filter = pointwise_filter;
this.bias = bias;
}
return SeparableConvParams;
}());
exports.SeparableConvParams = SeparableConvParams;
var SizeType;
(function (SizeType) {
SizeType["XS"] = "xs";
......
build/tinyYolov2/types.js.map
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\ No newline at end of file
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\ No newline at end of file
build/utils.d.ts
......@@ -3,6 +3,8 @@ import { Dimensions } 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 sigmoid(x: number): number;
export declare function isDimensions(obj: any): boolean;
export declare function resolveInput(arg: string | any): any;
export declare function isLoaded(media: HTMLImageElement | HTMLVideoElement): boolean;
export declare function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement): Promise<{}>;
......
build/utils.js
......@@ -15,6 +15,14 @@ function round(num) {
return Math.floor(num * 100) / 100;
}
exports.round = round;
function sigmoid(x) {
return 1 / (1 + Math.exp(-x));
}
exports.sigmoid = sigmoid;
function isDimensions(obj) {
return obj && obj.width && obj.height;
}
exports.isDimensions = isDimensions;
function resolveInput(arg) {
if (typeof arg === 'string') {
return document.getElementById(arg);
......@@ -37,14 +45,14 @@ function awaitMediaLoaded(media) {
return;
e.currentTarget.removeEventListener('load', onLoad);
e.currentTarget.removeEventListener('error', onError);
resolve();
resolve(e);
}
function onError(e) {
if (!e.currentTarget)
return;
e.currentTarget.removeEventListener('load', onLoad);
e.currentTarget.removeEventListener('error', onError);
reject();
reject(e);
}
media.addEventListener('load', onLoad);
media.addEventListener('error', onError);
......
build/utils.js.map
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dist/face-api.js.map
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dist/face-api.min.js
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