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Commit cd43f793 by vincent
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check in latest build

parent 2ce95197
Showing with 667 additions and 35 deletions
build/BoundingBox.d.ts 0 → 100644
import { Rect } from './Rect';
export declare class BoundingBox {
private _left;
private _top;
private _right;
private _bottom;
constructor(_left: number, _top: number, _right: number, _bottom: number);
readonly left: number;
readonly top: number;
readonly right: number;
readonly bottom: number;
readonly width: number;
readonly height: number;
toSquare(): BoundingBox;
round(): BoundingBox;
padAtBorders(imageHeight: number, imageWidth: number): {
dy: number;
edy: number;
dx: number;
edx: number;
y: number;
ey: number;
x: number;
ex: number;
w: number;
h: number;
};
calibrate(region: BoundingBox): BoundingBox;
toRect(): Rect;
}
build/BoundingBox.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("./Rect");
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
this._top = _top;
this._right = _right;
this._bottom = _bottom;
}
Object.defineProperty(BoundingBox.prototype, "left", {
get: function () {
return this._left;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "top", {
get: function () {
return this._top;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "right", {
get: function () {
return this._right;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "bottom", {
get: function () {
return this._bottom;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "width", {
get: function () {
return this.right - this.left;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "height", {
get: function () {
return this.bottom - this.top;
},
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);
if (this.width < this.height) {
left -= off;
right += off;
}
if (this.height < this.width) {
top -= off;
bottom += off;
}
return new BoundingBox(left, top, right, bottom);
};
BoundingBox.prototype.round = function () {
return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom));
};
BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) {
var w = this.width + 1;
var h = this.height + 1;
var dx = 1;
var dy = 1;
var edx = w;
var edy = h;
var x = this.left;
var y = this.top;
var ex = this.right;
var ey = this.bottom;
if (ex > imageWidth) {
edx = -ex + imageWidth + w;
ex = imageWidth;
}
if (ey > imageHeight) {
edy = -ey + imageHeight + h;
ey = imageHeight;
}
if (x < 1) {
edy = 2 - x;
x = 1;
}
if (y < 1) {
edy = 2 - y;
y = 1;
}
return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
};
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.toRect = function () {
return new Rect_1.Rect(this.left, this.top, this.width, this.height);
};
return BoundingBox;
}());
exports.BoundingBox = BoundingBox;
//# sourceMappingURL=BoundingBox.js.map
\ No newline at end of file
build/BoundingBox.js.map 0 → 100644
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\ No newline at end of file
build/commons/nonMaxSuppression.d.ts 0 → 100644
import { BoundingBox } from '../BoundingBox';
export declare function nonMaxSuppression(boxes: BoundingBox[], scores: number[], iouThreshold: number, isIOU?: boolean): number[];
build/commons/nonMaxSuppression.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
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; })
.map(function (c) { return c.boxIndex; });
var pick = [];
var _loop_1 = function () {
var curr = indicesSortedByScore.pop();
pick.push(curr);
var indices = indicesSortedByScore;
var outputs = [];
for (var i = 0; i < indices.length; i++) {
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);
}
indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
};
while (indicesSortedByScore.length > 0) {
_loop_1();
}
return pick;
}
exports.nonMaxSuppression = nonMaxSuppression;
//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file
build/commons/nonMaxSuppression.js.map 0 → 100644
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\ No newline at end of file
build/faceDetectionNet/loadQuantizedParams.js
......@@ -5,7 +5,7 @@ var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensor
var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
var isTensor_1 = require("../commons/isTensor");
var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'face_detection_model';
var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
......
build/faceDetectionNet/loadQuantizedParams.js.map
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\ No newline at end of file
build/globalApi.d.ts
......@@ -8,6 +8,8 @@ import { FullFaceDescription } from './FullFaceDescription';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { NetInput } from './NetInput';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
import { TinyYolov2ForwardParams } from './tinyYolov2/types';
import { TNetInput } from './types';
export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmarkNet;
......@@ -17,16 +19,19 @@ export declare const nets: {
faceLandmark68Net: FaceLandmarkNet;
faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn;
tinyYolov2: TinyYolov2;
};
export declare function loadFaceDetectionModel(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 loadModels(url: string): Promise<[void, void, void, void]>;
export declare function loadTinyYolov2Model(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 allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
......
build/globalApi.js
......@@ -5,6 +5,7 @@ var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn");
var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
exports.landmarkNet = new FaceLandmarkNet_1.FaceLandmarkNet();
exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
......@@ -14,7 +15,8 @@ exports.nets = {
ssdMobilenet: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet,
faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn()
mtcnn: new Mtcnn_1.Mtcnn(),
tinyYolov2: new TinyYolov2_1.TinyYolov2()
};
function loadFaceDetectionModel(url) {
return exports.nets.ssdMobilenet.load(url);
......@@ -32,12 +34,17 @@ function loadMtcnnModel(url) {
return exports.nets.mtcnn.load(url);
}
exports.loadMtcnnModel = loadMtcnnModel;
function loadTinyYolov2Model(url) {
return exports.nets.tinyYolov2.load(url);
}
exports.loadTinyYolov2Model = loadTinyYolov2Model;
function loadModels(url) {
return Promise.all([
loadFaceDetectionModel(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url),
loadMtcnnModel(url)
loadMtcnnModel(url),
loadTinyYolov2Model(url)
]);
}
exports.loadModels = loadModels;
......@@ -57,6 +64,10 @@ function mtcnn(input, forwardParams) {
return exports.nets.mtcnn.forward(input, forwardParams);
}
exports.mtcnn = mtcnn;
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.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
//# 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
......@@ -15,5 +15,6 @@ export * from './faceRecognitionNet';
export * from './globalApi';
export * from './mtcnn';
export * from './padToSquare';
export * from './tinyYolov2';
export * from './toNetInput';
export * from './utils';
build/index.js
......@@ -18,6 +18,7 @@ tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
tslib_1.__exportStar(require("./globalApi"), exports);
tslib_1.__exportStar(require("./mtcnn"), exports);
tslib_1.__exportStar(require("./padToSquare"), exports);
tslib_1.__exportStar(require("./tinyYolov2"), exports);
tslib_1.__exportStar(require("./toNetInput"), exports);
tslib_1.__exportStar(require("./utils"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/index.js.map
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\ No newline at end of file
build/mtcnn/BoundingBox.d.ts
import { Rect } from '../Rect';
export declare class BoundingBox {
private _left;
private _top;
......@@ -25,4 +26,5 @@ export declare class BoundingBox {
h: number;
};
calibrate(region: BoundingBox): BoundingBox;
toRect(): Rect;
}
build/mtcnn/BoundingBox.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("../Rect");
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
......@@ -97,6 +98,9 @@ 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.toRect = function () {
return new Rect_1.Rect(this.left, this.top, this.width, this.height);
};
return BoundingBox;
}());
exports.BoundingBox = BoundingBox;
......
build/mtcnn/BoundingBox.js.map
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\ No newline at end of file
build/mtcnn/extractImagePatches.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from '../BoundingBox';
import { Dimensions } from '../types';
import { BoundingBox } from './BoundingBox';
export declare function extractImagePatches(img: HTMLCanvasElement, boxes: BoundingBox[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>;
build/mtcnn/extractImagePatches.js.map
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build/mtcnn/stage1.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from './BoundingBox';
import { BoundingBox } from '../BoundingBox';
import { PNetParams } from './types';
export declare function stage1(imgTensor: tf.Tensor4D, scales: number[], scoreThreshold: number, params: PNetParams, stats: any): {
boxes: BoundingBox[];
......
build/mtcnn/stage1.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var BoundingBox_1 = require("../BoundingBox");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var Point_1 = require("../Point");
var BoundingBox_1 = require("./BoundingBox");
var config_1 = require("./config");
var nms_1 = require("./nms");
var getSizesForScale_1 = require("./getSizesForScale");
var normalize_1 = require("./normalize");
var PNet_1 = require("./PNet");
var getSizesForScale_1 = require("./getSizesForScale");
function rescaleAndNormalize(x, scale) {
return tf.tidy(function () {
var _a = getSizesForScale_1.getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
......@@ -65,7 +65,7 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
return [];
}
var ts = Date.now();
var indices = nms_1.nms(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
var indices = nonMaxSuppression_1.nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
statsForScale.nms = Date.now() - ts;
statsForScale.numBoxes = indices.length;
stats.stage1.push(statsForScale);
......@@ -76,7 +76,7 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
var finalScores = [];
if (allBoxes.length > 0) {
var ts = Date.now();
var indices = nms_1.nms(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
var indices = nonMaxSuppression_1.nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
stats.stage1_nms = Date.now() - ts;
finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
finalBoxes = indices
......
build/mtcnn/stage1.js.map
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build/mtcnn/stage2.d.ts
import { BoundingBox } from './BoundingBox';
import { BoundingBox } from '../BoundingBox';
import { RNetParams } from './types';
export declare function stage2(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: RNetParams, stats: any): Promise<{
boxes: BoundingBox[];
......
build/mtcnn/stage2.js
......@@ -2,9 +2,9 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var BoundingBox_1 = require("./BoundingBox");
var BoundingBox_1 = require("../BoundingBox");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var extractImagePatches_1 = require("./extractImagePatches");
var nms_1 = require("./nms");
var RNet_1 = require("./RNet");
function stage2(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
......@@ -45,7 +45,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
finalScores = [];
if (filteredBoxes.length > 0) {
ts = Date.now();
indicesNms = nms_1.nms(filteredBoxes, filteredScores, 0.7);
indicesNms = nonMaxSuppression_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
stats.stage2_nms = Date.now() - ts;
regions_1 = indicesNms.map(function (idx) {
return new BoundingBox_1.BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
......
build/mtcnn/stage2.js.map
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\ No newline at end of file
build/mtcnn/stage3.d.ts
import { BoundingBox } from '../BoundingBox';
import { Point } from '../Point';
import { BoundingBox } from './BoundingBox';
import { ONetParams } from './types';
export declare function stage3(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: ONetParams, stats: any): Promise<{
boxes: BoundingBox[];
......
build/mtcnn/stage3.js
......@@ -2,10 +2,10 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var BoundingBox_1 = require("../BoundingBox");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var Point_1 = require("../Point");
var BoundingBox_1 = require("./BoundingBox");
var extractImagePatches_1 = require("./extractImagePatches");
var nms_1 = require("./nms");
var ONet_1 = require("./ONet");
function stage3(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
......@@ -49,7 +49,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
points = [];
if (filteredBoxes.length > 0) {
ts = Date.now();
indicesNms = nms_1.nms(filteredBoxes, filteredScores, 0.7, false);
indicesNms = nonMaxSuppression_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
stats.stage3_nms = Date.now() - ts;
finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
......
build/mtcnn/stage3.js.map
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\ No newline at end of file
build/tinyYolov2/TinyYolov2.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { FaceDetection } from '../FaceDetection';
import { NetInput } from '../NetInput';
import { TNetInput } from '../types';
import { NetParams, TinyYolov2ForwardParams } from './types';
export declare class TinyYolov2 extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput, inputSize: number): tf.Tensor4D;
forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D>;
locateFaces(input: TNetInput, forwardParams?: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/tinyYolov2/TinyYolov2.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var BoundingBox_1 = require("../BoundingBox");
var convLayer_1 = require("../commons/convLayer");
var NeuralNetwork_1 = require("../commons/NeuralNetwork");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var FaceDetection_1 = require("../FaceDetection");
var toNetInput_1 = require("../toNetInput");
var config_1 = require("./config");
var convWithBatchNorm_1 = require("./convWithBatchNorm");
var extractParams_1 = require("./extractParams");
var getDefaultParams_1 = require("./getDefaultParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var TinyYolov2 = /** @class */ (function (_super) {
tslib_1.__extends(TinyYolov2, _super);
function TinyYolov2() {
return _super.call(this, 'TinyYolov2') || this;
}
TinyYolov2.prototype.forwardInput = function (input, inputSize) {
var params = this.params;
if (!params) {
throw new Error('TinyYolov2 - load model before inference');
}
var out = tf.tidy(function () {
var batchTensor = input.toBatchTensor(inputSize, false).div(tf.scalar(255)).toFloat();
var out = convWithBatchNorm_1.convWithBatchNorm(batchTensor, params.conv0);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv1);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv2);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv3);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv4);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv5);
out = tf.maxPool(out, [2, 2], [1, 1], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv6);
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv7);
out = convLayer_1.convLayer(out, params.conv8, 'valid', false);
return out;
});
return out;
};
TinyYolov2.prototype.forward = function (input, inputSize) {
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*/, toNetInput_1.toNetInput(input, true, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
case 2: return [2 /*return*/, _b.sent()];
}
});
});
};
TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, _inputSize, scoreThreshold, inputSize, netInput, out, numCells, _b, boxesTensor, scoresTensor, expit, paddedHeightRelative, paddedWidthRelative, boxes, scores, row, col, box, score, ctX, ctY, width, height, x, y, indices, detections;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
_a = getDefaultParams_1.getDefaultParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
inputSize = typeof _inputSize === 'string'
? config_1.INPUT_SIZES[_inputSize]
: _inputSize;
if (typeof inputSize !== 'number') {
throw new Error("TinyYolov2 - unkown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
}
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _c.sent();
return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
case 2:
out = _c.sent();
numCells = out.shape[1];
_b = tf.tidy(function () {
var reshaped = out.reshape([numCells, numCells, config_1.NUM_BOXES, 6]);
out.dispose();
var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, config_1.NUM_BOXES, 4]);
var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, config_1.NUM_BOXES, 1]);
return [boxes, scores];
}), boxesTensor = _b[0], scoresTensor = _b[1];
expit = function (x) { return 1 / (1 + Math.exp(-x)); };
paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
boxes = [];
scores = [];
for (row = 0; row < numCells; row++) {
for (col = 0; col < numCells; col++) {
for (box = 0; box < config_1.NUM_BOXES; box++) {
score = expit(scoresTensor.get(row, col, box, 0));
if (score > scoreThreshold) {
ctX = ((col + expit(boxesTensor.get(row, col, box, 0))) / numCells) * paddedWidthRelative;
ctY = ((row + expit(boxesTensor.get(row, col, box, 1))) / numCells) * paddedHeightRelative;
width = ((Math.exp(boxesTensor.get(row, col, box, 2)) * config_1.BOX_ANCHORS[box].x) / numCells) * paddedWidthRelative;
height = ((Math.exp(boxesTensor.get(row, col, box, 3)) * config_1.BOX_ANCHORS[box].y) / numCells) * paddedHeightRelative;
x = (ctX - (width / 2));
y = (ctY - (height / 2));
boxes.push(new BoundingBox_1.BoundingBox(x, y, x + width, y + height));
scores.push(score);
}
}
}
}
boxesTensor.dispose();
scoresTensor.dispose();
indices = nonMaxSuppression_1.nonMaxSuppression(boxes.map(function (box) { return new BoundingBox_1.BoundingBox(box.left * inputSize, box.top * inputSize, box.right * inputSize, box.bottom * inputSize); }), scores, config_1.IOU_THRESHOLD, true);
detections = indices.map(function (idx) {
return new FaceDetection_1.FaceDetection(scores[idx], boxes[idx].toRect(), { width: netInput.getInputWidth(0), height: netInput.getInputHeight(0) });
});
return [2 /*return*/, detections];
}
});
});
};
TinyYolov2.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
TinyYolov2.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return TinyYolov2;
}(NeuralNetwork_1.NeuralNetwork));
exports.TinyYolov2 = TinyYolov2;
//# sourceMappingURL=TinyYolov2.js.map
\ No newline at end of file
build/tinyYolov2/TinyYolov2.js.map 0 → 100644
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build/tinyYolov2/config.d.ts 0 → 100644
import { Point } from '../Point';
export declare const INPUT_SIZES: {
xs: number;
sm: number;
md: number;
lg: number;
};
export declare const NUM_BOXES = 5;
export declare const IOU_THRESHOLD = 0.4;
export declare const BOX_ANCHORS: Point[];
build/tinyYolov2/config.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Point_1 = require("../Point");
exports.INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 };
exports.NUM_BOXES = 5;
exports.IOU_THRESHOLD = 0.4;
exports.BOX_ANCHORS = [
new Point_1.Point(0.738768, 0.874946),
new Point_1.Point(2.42204, 2.65704),
new Point_1.Point(4.30971, 7.04493),
new Point_1.Point(10.246, 4.59428),
new Point_1.Point(12.6868, 11.8741)
];
//# sourceMappingURL=config.js.map
\ No newline at end of file
build/tinyYolov2/config.js.map 0 → 100644
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\ No newline at end of file
build/tinyYolov2/convWithBatchNorm.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvWithBatchNorm } from './types';
export declare function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D;
build/tinyYolov2/convWithBatchNorm.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var leaky_1 = require("./leaky");
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);
return leaky_1.leaky(out);
});
}
exports.convWithBatchNorm = convWithBatchNorm;
//# sourceMappingURL=convWithBatchNorm.js.map
\ No newline at end of file
build/tinyYolov2/convWithBatchNorm.js.map 0 → 100644
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\ No newline at end of file
build/tinyYolov2/extractParams.d.ts 0 → 100644
import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/tinyYolov2/extractParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
function extractorsFactory(extractWeights, paramMappings) {
var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
function extractBatchNormParams(size, mappedPrefix) {
var sub = tf.tensor1d(extractWeights(size));
var truediv = tf.tensor1d(extractWeights(size));
paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
return { sub: sub, truediv: truediv };
}
function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
return { conv: conv, bn: bn };
}
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams
};
}
function extractParams(weights) {
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');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
var params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
return { params: params, paramMappings: paramMappings };
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/tinyYolov2/extractParams.js.map 0 → 100644
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\ No newline at end of file
build/tinyYolov2/getDefaultParams.d.ts 0 → 100644
import { SizeType, TinyYolov2ForwardParams } from './types';
export declare function getDefaultParams(params: TinyYolov2ForwardParams): {
sizeType: SizeType;
scoreThreshold: number;
} & TinyYolov2ForwardParams;
build/tinyYolov2/getDefaultParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var types_1 = require("./types");
function getDefaultParams(params) {
return Object.assign({}, {
sizeType: types_1.SizeType.MD,
scoreThreshold: 0.5
}, params);
}
exports.getDefaultParams = getDefaultParams;
//# sourceMappingURL=getDefaultParams.js.map
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build/tinyYolov2/getDefaultParams.js.map 0 → 100644
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build/tinyYolov2/index.d.ts 0 → 100644
import { TinyYolov2 } from './TinyYolov2';
export * from './TinyYolov2';
export declare function createTinyYolov2(weights: Float32Array): TinyYolov2;
build/tinyYolov2/index.js 0 → 100644
"use strict";
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();
net.extractWeights(weights);
return net;
}
exports.createTinyYolov2 = createTinyYolov2;
//# sourceMappingURL=index.js.map
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build/tinyYolov2/index.js.map 0 → 100644
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build/tinyYolov2/leaky.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export declare function leaky(x: tf.Tensor4D): tf.Tensor4D;
build/tinyYolov2/leaky.js 0 → 100644
"use strict";
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)));
});
}
exports.leaky = leaky;
//# sourceMappingURL=leaky.js.map
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build/tinyYolov2/leaky.js.map 0 → 100644
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build/tinyYolov2/loadQuantizedParams.d.ts 0 → 100644
import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
build/tinyYolov2/loadQuantizedParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractBatchNormParams(prefix) {
var sub = extractWeightEntry(prefix + "/sub", 1);
var truediv = extractWeightEntry(prefix + "/truediv", 1);
return { sub: sub, truediv: truediv };
}
function extractConvParams(prefix) {
var filters = extractWeightEntry(prefix + "/filters", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return { filters: filters, bias: bias };
}
function extractConvWithBatchNormParams(prefix) {
var conv = extractConvParams(prefix + "/conv");
var bn = extractBatchNormParams(prefix + "/bn");
return { conv: conv, bn: bn };
}
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams
};
}
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.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'),
conv8: extractConvParams('conv8')
};
disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
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build/tinyYolov2/loadQuantizedParams.js.map 0 → 100644
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build/tinyYolov2/types.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare type BatchNorm = {
sub: tf.Tensor1D;
truediv: 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;
conv8: ConvParams;
};
export declare enum SizeType {
XS = "xs",
SM = "sm",
MD = "md",
LG = "lg",
}
export declare type TinyYolov2ForwardParams = {
inputSize?: SizeType | number;
scoreThreshold?: number;
};
build/tinyYolov2/types.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var SizeType;
(function (SizeType) {
SizeType["XS"] = "xs";
SizeType["SM"] = "sm";
SizeType["MD"] = "md";
SizeType["LG"] = "lg";
})(SizeType = exports.SizeType || (exports.SizeType = {}));
//# sourceMappingURL=types.js.map
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build/tinyYolov2/types.js.map 0 → 100644
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dist/face-api.js.map
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dist/face-api.min.js
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