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Commit 0381e197 by vincent
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check in latest build

parent 5fefdcd5
Showing with 968 additions and 554 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;
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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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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/TinyYolov2.js
......@@ -6,8 +6,10 @@ var BoundingBox_1 = require("../BoundingBox");
var convLayer_1 = require("../commons/convLayer");
var NeuralNetwork_1 = require("../commons/NeuralNetwork");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var normalize_1 = require("../commons/normalize");
var FaceDetection_1 = require("../FaceDetection");
var toNetInput_1 = require("../toNetInput");
var utils_1 = require("../utils");
var config_1 = require("./config");
var convWithBatchNorm_1 = require("./convWithBatchNorm");
var extractParams_1 = require("./extractParams");
......@@ -15,16 +17,39 @@ 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;
function TinyYolov2(withSeparableConvs) {
if (withSeparableConvs === void 0) { withSeparableConvs = true; }
var _this = _super.call(this, 'TinyYolov2') || this;
_this._withSeparableConvs = withSeparableConvs;
_this._anchors = withSeparableConvs ? config_1.BOX_ANCHORS_SEPARABLE : config_1.BOX_ANCHORS;
return _this;
}
Object.defineProperty(TinyYolov2.prototype, "withSeparableConvs", {
get: function () {
return this._withSeparableConvs;
},
enumerable: true,
configurable: true
});
Object.defineProperty(TinyYolov2.prototype, "anchors", {
get: function () {
return this._anchors;
},
enumerable: true,
configurable: true
});
TinyYolov2.prototype.forwardInput = function (input, inputSize) {
var _this = this;
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 batchTensor = input.toBatchTensor(inputSize, false);
batchTensor = _this.withSeparableConvs
? normalize_1.normalize(batchTensor, config_1.MEAN_RGB)
: batchTensor;
batchTensor = batchTensor.div(tf.scalar(256));
var out = convWithBatchNorm_1.convWithBatchNorm(batchTensor, params.conv0);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv1);
......@@ -61,69 +86,85 @@ var TinyYolov2 = /** @class */ (function (_super) {
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) {
var _a, _inputSize, scoreThreshold, inputSize, netInput, out, out0, inputDimensions, paddings, results, boxes, scores, indices, detections;
return tslib_1.__generator(this, function (_b) {
switch (_b.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");
throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
}
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _c.sent();
netInput = _b.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 = _b.sent();
out0 = tf.tidy(function () { return tf.unstack(out)[0].expandDims(); });
inputDimensions = {
width: netInput.getInputWidth(0),
height: netInput.getInputHeight(0)
};
paddings = netInput.getRelativePaddings(0);
results = this.postProcess(out0, { scoreThreshold: scoreThreshold, paddings: paddings });
boxes = results.map(function (res) { return res.box; });
scores = results.map(function (res) { return res.score; });
out.dispose();
out0.dispose();
indices = nonMaxSuppression_1.nonMaxSuppression(boxes.map(function (box) { return box.rescale(inputSize); }), scores, config_1.IOU_THRESHOLD, true);
detections = indices.map(function (idx) {
return new FaceDetection_1.FaceDetection(scores[idx], boxes[idx].toRect(), inputDimensions);
});
return [2 /*return*/, detections];
}
});
});
};
TinyYolov2.prototype.postProcess = function (outputTensor, _a) {
var _this = this;
var scoreThreshold = _a.scoreThreshold, paddings = _a.paddings;
var numCells = outputTensor.shape[1];
var _b = tf.tidy(function () {
var reshaped = outputTensor.reshape([numCells, numCells, config_1.NUM_BOXES, _this.withSeparableConvs ? 5 : 6]);
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);
var results = [];
for (var row = 0; row < numCells; row++) {
for (var col = 0; col < numCells; col++) {
for (var anchor = 0; anchor < config_1.NUM_BOXES; anchor++) {
var score = utils_1.sigmoid(scoresTensor.get(row, col, anchor, 0));
if (!scoreThreshold || score > scoreThreshold) {
var ctX = ((col + utils_1.sigmoid(boxesTensor.get(row, col, anchor, 0))) / numCells) * paddings.x;
var ctY = ((row + utils_1.sigmoid(boxesTensor.get(row, col, anchor, 1))) / numCells) * paddings.y;
var width = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.anchors[anchor].x) / numCells) * paddings.x;
var height = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.anchors[anchor].y) / numCells) * paddings.y;
var x = (ctX - (width / 2));
var y = (ctY - (height / 2));
results.push({
box: new BoundingBox_1.BoundingBox(x, y, x + width, y + height),
score: score,
row: row,
col: col,
anchor: anchor
});
}
}
}
}
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];
}
});
});
return results;
};
TinyYolov2.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
return loadQuantizedParams_1.loadQuantizedParams(uri, this.withSeparableConvs);
};
TinyYolov2.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
return extractParams_1.extractParams(weights, this.withSeparableConvs);
};
return TinyYolov2;
}(NeuralNetwork_1.NeuralNetwork));
......
build/tinyYolov2/TinyYolov2.js.map
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\ No newline at end of file
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\ No newline at end of file
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
build/tinyYolov2/config.js.map
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\ 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]]);
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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\ No newline at end of file
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\ No newline at end of file
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
{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/tinyYolov2/index.ts"],"names":[],"mappings":";;;AAAA,2CAA0C;AAE1C,uDAA6B;AAE7B,0BAAiC,OAAqB,EAAE,kBAAkC;IAAlC,mCAAA,EAAA,yBAAkC;IACxF,IAAM,GAAG,GAAG,IAAI,uBAAU,CAAC,kBAAkB,CAAC,CAAA;IAC9C,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,4CAIC"}
\ 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
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
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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\ No newline at end of file
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\ No newline at end of file
dist/face-api.js
......@@ -276,6 +276,9 @@
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(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);
......@@ -289,182 +292,6 @@
return Rect;
}());
var FaceDetection = /** @class */ (function () {
function FaceDetection(score, relativeBox, imageDims) {
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._score = score;
this._box = new Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
}
Object.defineProperty(FaceDetection.prototype, "score", {
get: function () {
return this._score;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "box", {
get: function () {
return this._box;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "imageWidth", {
get: function () {
return this._imageWidth;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "imageHeight", {
get: function () {
return this._imageHeight;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "relativeBox", {
get: function () {
return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
},
enumerable: true,
configurable: true
});
FaceDetection.prototype.getScore = function () {
return this.score;
};
FaceDetection.prototype.getBox = function () {
return this.box;
};
FaceDetection.prototype.getImageWidth = function () {
return this.imageWidth;
};
FaceDetection.prototype.getImageHeight = function () {
return this.imageHeight;
};
FaceDetection.prototype.getRelativeBox = function () {
return this.relativeBox;
};
FaceDetection.prototype.forSize = function (width, height) {
return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
};
return FaceDetection;
}());
var FullFaceDescription = /** @class */ (function () {
function FullFaceDescription(_detection, _landmarks, _descriptor) {
this._detection = _detection;
this._landmarks = _landmarks;
this._descriptor = _descriptor;
}
Object.defineProperty(FullFaceDescription.prototype, "detection", {
get: function () {
return this._detection;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
get: function () {
return this._landmarks;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
get: function () {
return this._descriptor;
},
enumerable: true,
configurable: true
});
FullFaceDescription.prototype.forSize = function (width, height) {
return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
};
return FullFaceDescription;
}());
function isTensor(tensor$$1, dim) {
return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
}
function isTensor2D(tensor$$1) {
return isTensor(tensor$$1, 2);
}
function isTensor3D(tensor$$1) {
return isTensor(tensor$$1, 3);
}
function isTensor4D(tensor$$1) {
return isTensor(tensor$$1, 4);
}
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
* @returns The padded tensor with width === height.
*/
function padToSquare(imgTensor, isCenterImage) {
if (isCenterImage === void 0) { isCenterImage = false; }
return tidy(function () {
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
if (height === width) {
return imgTensor;
}
var dimDiff = Math.abs(height - width);
var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
var paddingAxis = height > width ? 2 : 1;
var createPaddingTensor = function (paddingAmount) {
var paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount;
return fill(paddingTensorShape, 0);
};
var paddingTensorAppend = createPaddingTensor(paddingAmount);
var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
? createPaddingTensor(remainingPaddingAmount)
: null;
var tensorsToStack = [
paddingTensorPrepend,
imgTensor,
paddingTensorAppend
]
.filter(function (t) { return t !== null; });
return concat(tensorsToStack, paddingAxis);
});
}
var Point = /** @class */ (function () {
function Point(x, y) {
this.x = x;
this.y = y;
}
Point.prototype.add = function (pt) {
return new Point(this.x + pt.x, this.y + pt.y);
};
Point.prototype.sub = function (pt) {
return new Point(this.x - pt.x, this.y - pt.y);
};
Point.prototype.mul = function (pt) {
return new Point(this.x * pt.x, this.y * pt.y);
};
Point.prototype.div = function (pt) {
return new Point(this.x / pt.x, this.y / pt.y);
};
Point.prototype.abs = function () {
return new Point(Math.abs(this.x), Math.abs(this.y));
};
Point.prototype.magnitude = function () {
return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
};
Point.prototype.floor = function () {
return new Point(Math.floor(this.x), Math.floor(this.y));
};
return Point;
}());
/*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
......@@ -536,6 +363,19 @@
}
}
function isTensor(tensor$$1, dim) {
return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
}
function isTensor2D(tensor$$1) {
return isTensor(tensor$$1, 2);
}
function isTensor3D(tensor$$1) {
return isTensor(tensor$$1, 3);
}
function isTensor4D(tensor$$1) {
return isTensor(tensor$$1, 4);
}
function isFloat(num) {
return num % 1 !== 0;
}
......@@ -545,6 +385,12 @@
function round$1(num) {
return Math.floor(num * 100) / 100;
}
function sigmoid$1(x) {
return 1 / (1 + Math.exp(-x));
}
function isDimensions(obj) {
return obj && obj.width && obj.height;
}
function resolveInput(arg) {
if (typeof arg === 'string') {
return document.getElementById(arg);
......@@ -565,14 +411,14 @@
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);
......@@ -645,6 +491,284 @@
});
}
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
});
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);
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.rescale = function (s) {
var scaleX = isDimensions(s) ? s.width : s;
var scaleY = 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(this.left, this.top, this.width, this.height);
};
return BoundingBox;
}());
var FaceDetection = /** @class */ (function () {
function FaceDetection(score, relativeBox, imageDims) {
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._score = score;
this._box = new Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
}
Object.defineProperty(FaceDetection.prototype, "score", {
get: function () {
return this._score;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "box", {
get: function () {
return this._box;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "imageWidth", {
get: function () {
return this._imageWidth;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "imageHeight", {
get: function () {
return this._imageHeight;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FaceDetection.prototype, "relativeBox", {
get: function () {
return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
},
enumerable: true,
configurable: true
});
FaceDetection.prototype.getScore = function () {
return this.score;
};
FaceDetection.prototype.getBox = function () {
return this.box;
};
FaceDetection.prototype.getImageWidth = function () {
return this.imageWidth;
};
FaceDetection.prototype.getImageHeight = function () {
return this.imageHeight;
};
FaceDetection.prototype.getRelativeBox = function () {
return this.relativeBox;
};
FaceDetection.prototype.forSize = function (width, height) {
return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
};
return FaceDetection;
}());
var FullFaceDescription = /** @class */ (function () {
function FullFaceDescription(_detection, _landmarks, _descriptor) {
this._detection = _detection;
this._landmarks = _landmarks;
this._descriptor = _descriptor;
}
Object.defineProperty(FullFaceDescription.prototype, "detection", {
get: function () {
return this._detection;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
get: function () {
return this._landmarks;
},
enumerable: true,
configurable: true
});
Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
get: function () {
return this._descriptor;
},
enumerable: true,
configurable: true
});
FullFaceDescription.prototype.forSize = function (width, height) {
return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
};
return FullFaceDescription;
}());
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
* @returns The padded tensor with width === height.
*/
function padToSquare(imgTensor, isCenterImage) {
if (isCenterImage === void 0) { isCenterImage = false; }
return tidy(function () {
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
if (height === width) {
return imgTensor;
}
var dimDiff = Math.abs(height - width);
var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
var paddingAxis = height > width ? 2 : 1;
var createPaddingTensor = function (paddingAmount) {
var paddingTensorShape = imgTensor.shape.slice();
paddingTensorShape[paddingAxis] = paddingAmount;
return fill(paddingTensorShape, 0);
};
var paddingTensorAppend = createPaddingTensor(paddingAmount);
var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
? createPaddingTensor(remainingPaddingAmount)
: null;
var tensorsToStack = [
paddingTensorPrepend,
imgTensor,
paddingTensorAppend
]
.filter(function (t) { return t !== null; });
return concat(tensorsToStack, paddingAxis);
});
}
var Point = /** @class */ (function () {
function Point(x, y) {
this.x = x;
this.y = y;
}
Point.prototype.add = function (pt) {
return new Point(this.x + pt.x, this.y + pt.y);
};
Point.prototype.sub = function (pt) {
return new Point(this.x - pt.x, this.y - pt.y);
};
Point.prototype.mul = function (pt) {
return new Point(this.x * pt.x, this.y * pt.y);
};
Point.prototype.div = function (pt) {
return new Point(this.x / pt.x, this.y / pt.y);
};
Point.prototype.abs = function () {
return new Point(Math.abs(this.x), Math.abs(this.y));
};
Point.prototype.magnitude = function () {
return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
};
Point.prototype.floor = function () {
return new Point(Math.floor(this.x), Math.floor(this.y));
};
return Point;
}());
var NetInput = /** @class */ (function () {
function NetInput(inputs, isBatchInput, keepCanvases) {
if (isBatchInput === void 0) { isBatchInput = false; }
......@@ -656,6 +780,7 @@
this._isBatchInput = false;
this._inputDimensions = [];
this._paddings = [];
this._inputSize = 0;
if (isTensor4D(inputs)) {
this._inputs = unstack(inputs);
}
......@@ -732,6 +857,29 @@
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];
};
......@@ -744,9 +892,21 @@
NetInput.prototype.getPaddings = function (batchIdx) {
return this._paddings[batchIdx];
};
NetInput.prototype.getRelativePaddings = function (batchIdx) {
return new 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 tidy(function () {
var inputTensors = _this._inputs.map(function (inputTensor) {
var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1];
......@@ -767,7 +927,7 @@
});
};
/**
* 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 () {
......@@ -2107,7 +2267,7 @@
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return __awaiter$1(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 __generator$1(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
......@@ -2126,18 +2286,17 @@
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = 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(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
......@@ -2169,6 +2328,17 @@
return createFaceDetectionNet(weights);
}
function normalize(x, meanRgb) {
return tidy(function () {
var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];
var avg_r = fill(x.shape.slice(0, 3).concat([1]), r);
var avg_g = fill(x.shape.slice(0, 3).concat([1]), g);
var avg_b = fill(x.shape.slice(0, 3).concat([1]), b);
var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
return sub(x, avg_rgb);
});
}
function scale(x, params) {
return add(mul(x, params.weights), params.biases);
}
......@@ -2356,16 +2526,6 @@
});
}
function normalize(x) {
return tidy(function () {
var avg_r = fill(x.shape.slice(0, 3).concat([1]), 122.782);
var avg_g = fill(x.shape.slice(0, 3).concat([1]), 117.001);
var avg_b = fill(x.shape.slice(0, 3).concat([1]), 104.298);
var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
return div(sub(x, avg_rgb), scalar(256));
});
}
function residual(x, params) {
var out = conv$1(x, params.conv1);
out = convNoRelu(out, params.conv2);
......@@ -2408,7 +2568,8 @@
}
return tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var normalized = normalize(batchTensor);
var meanRgb = [122.782, 117.001, 104.298];
var normalized = normalize(batchTensor, meanRgb).div(scalar(256));
var out = convDown(normalized, params.conv32_down);
out = maxPool(out, 3, 2, 'valid');
out = residual(out, params.conv32_1);
......@@ -2510,16 +2671,15 @@
});
};
}
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 __awaiter$1(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return __generator$1(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(input, detections)];
......@@ -2549,6 +2709,34 @@
});
};
}
function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
return function (input, minConfidence, useBatchProcessing) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return __awaiter$1(this, void 0, void 0, function () {
var detectFaces, allFaces;
return __generator$1(this, function (_a) {
detectFaces = function (input) { return ssdMobilenetv1.locateFaces(input, minConfidence); };
allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
return [2 /*return*/, allFaces(input, useBatchProcessing)];
});
});
};
}
function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
return function (input, forwardParams, useBatchProcessing) {
if (forwardParams === void 0) { forwardParams = {}; }
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return __awaiter$1(this, void 0, void 0, function () {
var detectFaces, allFaces;
return __generator$1(this, function (_a) {
detectFaces = function (input) { return tinyYolov2.locateFaces(input, forwardParams); };
allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
return [2 /*return*/, allFaces(input, useBatchProcessing)];
});
});
};
}
function allFacesMtcnnFactory(mtcnn, recognitionNet) {
var computeDescriptors = computeDescriptorsFactory(recognitionNet);
return function (input, mtcnnForwardParams, useBatchProcessing) {
......@@ -2770,112 +2958,18 @@
return scales;
}
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;
function iou(box1, box2, isIOU) {
if (isIOU === void 0) { isIOU = true; }
var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left) + 1);
var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top) + 1);
var interSection = width * height;
return isIOU
? interSection / (box1.area + box2.area - interSection)
: interSection / Math.min(box1.area, box2.area);
}
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(this.left, this.top, this.width, this.height);
};
return BoundingBox;
}());
function nonMaxSuppression$1(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; })
......@@ -2890,13 +2984,7 @@
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(currBox, idxBox, isIOU));
}
indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
};
......@@ -3366,20 +3454,52 @@
new Point(10.246, 4.59428),
new Point(12.6868, 11.8741)
];
var BOX_ANCHORS_SEPARABLE = [
new Point(1.603231, 2.094468),
new Point(6.041143, 7.080126),
new Point(2.882459, 3.518061),
new Point(4.266906, 5.178857),
new Point(9.041765, 10.66308)
];
var MEAN_RGB = [117.001, 114.697, 97.404];
function leaky(x) {
return tidy(function () {
return maximum(x, mul(x, scalar(0.10000000149011612)));
var min$$1 = mul(x, scalar(0.10000000149011612));
return add(relu(sub(x, min$$1)), min$$1);
//return tf.maximum(x, min)
});
}
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;
}());
var SizeType;
(function (SizeType) {
SizeType["XS"] = "xs";
SizeType["SM"] = "sm";
SizeType["MD"] = "md";
SizeType["LG"] = "lg";
})(SizeType || (SizeType = {}));
function convWithBatchNorm(x, params) {
return tidy(function () {
var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
if (params instanceof SeparableConvParams) {
out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
out = add(out, params.bias);
}
else {
out = conv2d(out, params.conv.filters, [1, 1], 'valid');
out = sub(out, params.bn.sub);
out = mul(out, params.bn.truediv);
out = add(out, params.conv.bias);
}
return leaky(out);
});
}
......@@ -3397,24 +3517,34 @@
var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
return { conv: conv, bn: bn };
}
function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams
extractConvWithBatchNormParams: extractConvWithBatchNormParams,
extractSeparableConvParams: extractSeparableConvParams
};
}
function extractParams$4(weights) {
function extractParams$4(weights, withSeparableConvs) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var paramMappings = [];
var _b = extractorsFactory$7(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$7(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);
}
......@@ -3422,22 +3552,15 @@
return { params: params, paramMappings: paramMappings };
}
var SizeType;
(function (SizeType) {
SizeType["XS"] = "xs";
SizeType["SM"] = "sm";
SizeType["MD"] = "md";
SizeType["LG"] = "lg";
})(SizeType || (SizeType = {}));
function getDefaultParams(params) {
return Object.assign({}, {
sizeType: SizeType.MD,
inputSize: SizeType.MD,
scoreThreshold: 0.5
}, params);
}
var DEFAULT_MODEL_NAME$4 = 'tiny_yolov2_model';
var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
function extractorsFactory$8(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractBatchNormParams(prefix) {
......@@ -3455,30 +3578,38 @@
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 SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams
extractConvWithBatchNormParams: extractConvWithBatchNormParams,
extractSeparableConvParams: extractSeparableConvParams
};
}
function loadQuantizedParams$4(uri) {
function loadQuantizedParams$4(uri, withSeparableConvs) {
return __awaiter$1(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, params;
var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams, extractConvFn, params;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$4)];
case 0: return [4 /*yield*/, loadWeightMap(uri, withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME$4)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory$8(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams;
_a = extractorsFactory$8(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(weightMap, paramMappings);
......@@ -3490,16 +3621,39 @@
var TinyYolov2 = /** @class */ (function (_super) {
__extends$1(TinyYolov2, _super);
function TinyYolov2() {
return _super.call(this, 'TinyYolov2') || this;
}
function TinyYolov2(withSeparableConvs) {
if (withSeparableConvs === void 0) { withSeparableConvs = true; }
var _this = _super.call(this, 'TinyYolov2') || this;
_this._withSeparableConvs = withSeparableConvs;
_this._anchors = withSeparableConvs ? BOX_ANCHORS_SEPARABLE : BOX_ANCHORS;
return _this;
}
Object.defineProperty(TinyYolov2.prototype, "withSeparableConvs", {
get: function () {
return this._withSeparableConvs;
},
enumerable: true,
configurable: true
});
Object.defineProperty(TinyYolov2.prototype, "anchors", {
get: function () {
return this._anchors;
},
enumerable: true,
configurable: true
});
TinyYolov2.prototype.forwardInput = function (input, inputSize) {
var _this = this;
var params = this.params;
if (!params) {
throw new Error('TinyYolov2 - load model before inference');
}
var out = tidy(function () {
var batchTensor = input.toBatchTensor(inputSize, false).div(scalar(255)).toFloat();
var batchTensor = input.toBatchTensor(inputSize, false);
batchTensor = _this.withSeparableConvs
? normalize(batchTensor, MEAN_RGB)
: batchTensor;
batchTensor = batchTensor.div(scalar(256));
var out = convWithBatchNorm(batchTensor, params.conv0);
out = maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm(out, params.conv1);
......@@ -3536,69 +3690,85 @@
TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return __awaiter$1(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 __generator$1(this, function (_c) {
switch (_c.label) {
var _a, _inputSize, scoreThreshold, inputSize, netInput, out, out0, inputDimensions, paddings, results, boxes, scores, indices, detections;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
_a = getDefaultParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
inputSize = typeof _inputSize === 'string'
? INPUT_SIZES[_inputSize]
: _inputSize;
if (typeof inputSize !== 'number') {
throw new Error("TinyYolov2 - unkown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
}
return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _c.sent();
netInput = _b.sent();
return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
case 2:
out = _c.sent();
numCells = out.shape[1];
_b = tidy(function () {
var reshaped = out.reshape([numCells, numCells, NUM_BOXES, 6]);
out = _b.sent();
out0 = tidy(function () { return unstack(out)[0].expandDims(); });
inputDimensions = {
width: netInput.getInputWidth(0),
height: netInput.getInputHeight(0)
};
paddings = netInput.getRelativePaddings(0);
results = this.postProcess(out0, { scoreThreshold: scoreThreshold, paddings: paddings });
boxes = results.map(function (res) { return res.box; });
scores = results.map(function (res) { return res.score; });
out.dispose();
out0.dispose();
indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, IOU_THRESHOLD, true);
detections = indices.map(function (idx) {
return new FaceDetection(scores[idx], boxes[idx].toRect(), inputDimensions);
});
return [2 /*return*/, detections];
}
});
});
};
TinyYolov2.prototype.postProcess = function (outputTensor, _a) {
var _this = this;
var scoreThreshold = _a.scoreThreshold, paddings = _a.paddings;
var numCells = outputTensor.shape[1];
var _b = tidy(function () {
var reshaped = outputTensor.reshape([numCells, numCells, NUM_BOXES, _this.withSeparableConvs ? 5 : 6]);
var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, NUM_BOXES, 4]);
var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, 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 < 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)) * BOX_ANCHORS[box].x) / numCells) * paddedWidthRelative;
height = ((Math.exp(boxesTensor.get(row, col, box, 3)) * BOX_ANCHORS[box].y) / numCells) * paddedHeightRelative;
x = (ctX - (width / 2));
y = (ctY - (height / 2));
boxes.push(new BoundingBox(x, y, x + width, y + height));
scores.push(score);
var results = [];
for (var row = 0; row < numCells; row++) {
for (var col = 0; col < numCells; col++) {
for (var anchor = 0; anchor < NUM_BOXES; anchor++) {
var score = sigmoid$1(scoresTensor.get(row, col, anchor, 0));
if (!scoreThreshold || score > scoreThreshold) {
var ctX = ((col + sigmoid$1(boxesTensor.get(row, col, anchor, 0))) / numCells) * paddings.x;
var ctY = ((row + sigmoid$1(boxesTensor.get(row, col, anchor, 1))) / numCells) * paddings.y;
var width = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.anchors[anchor].x) / numCells) * paddings.x;
var height = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.anchors[anchor].y) / numCells) * paddings.y;
var x = (ctX - (width / 2));
var y = (ctY - (height / 2));
results.push({
box: new BoundingBox(x, y, x + width, y + height),
score: score,
row: row,
col: col,
anchor: anchor
});
}
}
}
}
boxesTensor.dispose();
scoresTensor.dispose();
indices = nonMaxSuppression$1(boxes.map(function (box) { return new BoundingBox(box.left * inputSize, box.top * inputSize, box.right * inputSize, box.bottom * inputSize); }), scores, IOU_THRESHOLD, true);
detections = indices.map(function (idx) {
return new FaceDetection(scores[idx], boxes[idx].toRect(), { width: netInput.getInputWidth(0), height: netInput.getInputHeight(0) });
});
return [2 /*return*/, detections];
}
});
});
return results;
};
TinyYolov2.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams$4(uri);
return loadQuantizedParams$4(uri, this.withSeparableConvs);
};
TinyYolov2.prototype.extractParams = function (weights) {
return extractParams$4(weights);
return extractParams$4(weights, this.withSeparableConvs);
};
return TinyYolov2;
}(NeuralNetwork));
......@@ -3609,14 +3779,14 @@
// nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided
var nets = {
ssdMobilenet: detectionNet,
ssdMobilenetv1: detectionNet,
faceLandmark68Net: landmarkNet,
faceRecognitionNet: recognitionNet,
mtcnn: new Mtcnn(),
tinyYolov2: new TinyYolov2()
};
function loadFaceDetectionModel(url) {
return nets.ssdMobilenet.load(url);
function loadSsdMobilenetv1Model(url) {
return nets.ssdMobilenetv1.load(url);
}
function loadFaceLandmarkModel(url) {
return nets.faceLandmark68Net.load(url);
......@@ -3630,9 +3800,12 @@
function loadTinyYolov2Model(url) {
return nets.tinyYolov2.load(url);
}
function loadFaceDetectionModel(url) {
return loadSsdMobilenetv1Model(url);
}
function loadModels(url) {
return Promise.all([
loadFaceDetectionModel(url),
loadSsdMobilenetv1Model(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url),
loadMtcnnModel(url),
......@@ -3640,7 +3813,7 @@
]);
}
function locateFaces(input, minConfidence, maxResults) {
return nets.ssdMobilenet.locateFaces(input, minConfidence, maxResults);
return nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
}
function detectLandmarks(input) {
return nets.faceLandmark68Net.detectLandmarks(input);
......@@ -3654,8 +3827,10 @@
function tinyYolov2(input, forwardParams) {
return nets.tinyYolov2.locateFaces(input, forwardParams);
}
var allFaces = allFacesFactory(nets.ssdMobilenet, nets.faceLandmark68Net, nets.faceRecognitionNet);
var allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(nets.ssdMobilenetv1, nets.faceLandmark68Net, nets.faceRecognitionNet);
var allFacesTinyYolov2 = allFacesTinyYolov2Factory(nets.tinyYolov2, nets.faceLandmark68Net, nets.faceRecognitionNet);
var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet);
var allFaces = allFacesSsdMobilenetv1;
function createMtcnn(weights) {
var net = new Mtcnn();
......@@ -3663,13 +3838,15 @@
return net;
}
function createTinyYolov2(weights) {
var net = new TinyYolov2();
function createTinyYolov2(weights, withSeparableConvs) {
if (withSeparableConvs === void 0) { withSeparableConvs = true; }
var net = new TinyYolov2(withSeparableConvs);
net.extractWeights(weights);
return net;
}
exports.tf = tfCore_esm;
exports.BoundingBox = BoundingBox;
exports.FaceDetection = FaceDetection;
exports.FullFaceDescription = FullFaceDescription;
exports.NetInput = NetInput;
......@@ -3697,19 +3874,23 @@
exports.landmarkNet = landmarkNet;
exports.recognitionNet = recognitionNet;
exports.nets = nets;
exports.loadFaceDetectionModel = loadFaceDetectionModel;
exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
exports.loadMtcnnModel = loadMtcnnModel;
exports.loadTinyYolov2Model = loadTinyYolov2Model;
exports.loadFaceDetectionModel = loadFaceDetectionModel;
exports.loadModels = loadModels;
exports.locateFaces = locateFaces;
exports.detectLandmarks = detectLandmarks;
exports.computeFaceDescriptor = computeFaceDescriptor;
exports.mtcnn = mtcnn;
exports.tinyYolov2 = tinyYolov2;
exports.allFaces = allFaces;
exports.allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1;
exports.allFacesTinyYolov2 = allFacesTinyYolov2;
exports.allFacesMtcnn = allFacesMtcnn;
exports.allFaces = allFaces;
exports.iou = iou;
exports.createMtcnn = createMtcnn;
exports.Mtcnn = Mtcnn;
exports.FaceLandmarks5 = FaceLandmarks5;
......@@ -3720,6 +3901,8 @@
exports.isFloat = isFloat;
exports.isEven = isEven;
exports.round = round$1;
exports.sigmoid = sigmoid$1;
exports.isDimensions = isDimensions;
exports.resolveInput = resolveInput;
exports.isLoaded = isLoaded;
exports.awaitMediaLoaded = awaitMediaLoaded;
......
dist/face-api.js.map
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dist/face-api.min.js
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