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Commit ccdd16ec by vincent
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compile module to commonjs

parent f11445bb
Showing with 575 additions and 367 deletions
dist/NetInput.js
import { createCanvasFromMedia, getElement } from './utils';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var utils_1 = require("./utils");
var NetInput = /** @class */ (function () {
function NetInput(mediaArg, dims) {
var _this = this;
......@@ -8,7 +10,7 @@ var NetInput = /** @class */ (function () {
if (!mediaArgArray.length) {
throw new Error('NetInput - empty array passed as input');
}
var medias = mediaArgArray.map(getElement);
var medias = mediaArgArray.map(utils_1.getElement);
medias.forEach(function (media, i) {
if (!(media instanceof HTMLImageElement || media instanceof HTMLVideoElement || media instanceof HTMLCanvasElement)) {
var idxHint = Array.isArray(mediaArg) ? " at input index " + i + ":" : '';
......@@ -28,7 +30,7 @@ var NetInput = /** @class */ (function () {
}
// if input is batch type, make sure every canvas has the same dimensions
var canvasDims = this.dims || dims;
this._canvases.push(createCanvasFromMedia(media, canvasDims));
this._canvases.push(utils_1.createCanvasFromMedia(media, canvasDims));
};
Object.defineProperty(NetInput.prototype, "canvases", {
get: function () {
......@@ -61,5 +63,5 @@ var NetInput = /** @class */ (function () {
});
return NetInput;
}());
export { NetInput };
exports.NetInput = NetInput;
//# sourceMappingURL=NetInput.js.map
\ No newline at end of file
dist/NetInput.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/Point.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Point = /** @class */ (function () {
function Point(x, y) {
this.x = x;
......@@ -26,5 +28,5 @@ var Point = /** @class */ (function () {
};
return Point;
}());
export { Point };
exports.Point = Point;
//# sourceMappingURL=Point.js.map
\ No newline at end of file
dist/Point.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/Rect.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect = /** @class */ (function () {
function Rect(x, y, width, height) {
this.x = x;
......@@ -10,5 +12,5 @@ var Rect = /** @class */ (function () {
};
return Rect;
}());
export { Rect };
exports.Rect = Rect;
//# sourceMappingURL=Rect.js.map
\ No newline at end of file
dist/Rect.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/commons/convLayer.js
import * as tf from '@tensorflow/tfjs-core';
export function convLayer(x, params, padding, withRelu) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function convLayer(x, params, padding, withRelu) {
if (padding === void 0) { padding = 'same'; }
if (withRelu === void 0) { withRelu = false; }
return tf.tidy(function () {
......@@ -7,4 +9,5 @@ export function convLayer(x, params, padding, withRelu) {
return withRelu ? tf.relu(out) : out;
});
}
exports.convLayer = convLayer;
//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
dist/commons/convLayer.js.map
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\ No newline at end of file
{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../src/commons/convLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAI5C,mBACE,CAAc,EACd,MAAkB,EAClB,OAAkC,EAClC,QAAyB;IADzB,wBAAA,EAAA,gBAAkC;IAClC,yBAAA,EAAA,gBAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAChB,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,EAC7C,MAAM,CAAC,IAAI,CACG,CAAA;QAEhB,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;IACtC,CAAC,CAAC,CAAA;AACJ,CAAC;AAdD,8BAcC"}
\ No newline at end of file
dist/commons/extractConvParamsFactory.js
import * as tf from '@tensorflow/tfjs-core';
export function extractConvParamsFactory(extractWeights) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function extractConvParamsFactory(extractWeights) {
return function (channelsIn, channelsOut, filterSize) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
......@@ -9,4 +11,5 @@ export function extractConvParamsFactory(extractWeights) {
};
};
}
exports.extractConvParamsFactory = extractConvParamsFactory;
//# sourceMappingURL=extractConvParamsFactory.js.map
\ No newline at end of file
dist/commons/extractConvParamsFactory.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/commons/extractWeightsFactory.js
export function extractWeightsFactory(weights) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function extractWeightsFactory(weights) {
var remainingWeights = weights;
function extractWeights(numWeights) {
var ret = remainingWeights.slice(0, numWeights);
......@@ -13,4 +15,5 @@ export function extractWeightsFactory(weights) {
getRemainingWeights: getRemainingWeights
};
}
exports.extractWeightsFactory = extractWeightsFactory;
//# sourceMappingURL=extractWeightsFactory.js.map
\ No newline at end of file
dist/commons/extractWeightsFactory.js.map
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\ No newline at end of file
dist/commons/getCenterPoint.js
import { Point } from '../Point';
export function getCenterPoint(pts) {
return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))
.div(new Point(pts.length, pts.length));
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Point_1 = require("../Point");
function getCenterPoint(pts) {
return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point_1.Point(0, 0))
.div(new Point_1.Point(pts.length, pts.length));
}
exports.getCenterPoint = getCenterPoint;
//# sourceMappingURL=getCenterPoint.js.map
\ No newline at end of file
dist/commons/getCenterPoint.js.map
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\ No newline at end of file
dist/commons/isTensor.js
import * as tf from '@tensorflow/tfjs-core';
export function isTensor(tensor, dim) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function isTensor(tensor, dim) {
return tensor instanceof tf.Tensor && tensor.shape.length === dim;
}
export function isTensor1D(tensor) {
exports.isTensor = isTensor;
function isTensor1D(tensor) {
return isTensor(tensor, 1);
}
export function isTensor2D(tensor) {
exports.isTensor1D = isTensor1D;
function isTensor2D(tensor) {
return isTensor(tensor, 2);
}
export function isTensor3D(tensor) {
exports.isTensor2D = isTensor2D;
function isTensor3D(tensor) {
return isTensor(tensor, 3);
}
export function isTensor4D(tensor) {
exports.isTensor3D = isTensor3D;
function isTensor4D(tensor) {
return isTensor(tensor, 4);
}
exports.isTensor4D = isTensor4D;
//# sourceMappingURL=isTensor.js.map
\ No newline at end of file
dist/commons/isTensor.js.map
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\ No newline at end of file
dist/commons/loadWeightMap.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
export function getModelUris(uri, defaultModelName) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
function getModelUris(uri, defaultModelName) {
var parts = (uri || '').split('/');
var modelBaseUri = ((uri || '').endsWith('.json')
? parts.slice(0, parts.length - 1)
......@@ -13,7 +15,8 @@ export function getModelUris(uri, defaultModelName) {
: modelBaseUri + "/" + defaultManifestFilename);
return { manifestUri: manifestUri, modelBaseUri: modelBaseUri };
}
export function loadWeightMap(uri, defaultModelName) {
exports.getModelUris = getModelUris;
function loadWeightMap(uri, defaultModelName) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, manifestUri, modelBaseUri, manifest;
return tslib_1.__generator(this, function (_b) {
......@@ -29,4 +32,5 @@ export function loadWeightMap(uri, defaultModelName) {
});
});
}
exports.loadWeightMap = loadWeightMap;
//# sourceMappingURL=loadWeightMap.js.map
\ No newline at end of file
dist/commons/loadWeightMap.js.map
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\ No newline at end of file
dist/commons/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/euclideanDistance.js
export function euclideanDistance(arr1, arr2) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function euclideanDistance(arr1, arr2) {
if (arr1.length !== arr2.length)
throw new Error('euclideanDistance: arr1.length !== arr2.length');
var desc1 = Array.from(arr1);
......@@ -7,4 +9,5 @@ export function euclideanDistance(arr1, arr2) {
.map(function (val, i) { return val - desc2[i]; })
.reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
}
exports.euclideanDistance = euclideanDistance;
//# sourceMappingURL=euclideanDistance.js.map
\ No newline at end of file
dist/euclideanDistance.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/extractFaceTensors.js
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { getImageTensor } from './getImageTensor';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var FaceDetection_1 = require("./faceDetectionNet/FaceDetection");
var getImageTensor_1 = require("./getImageTensor");
/**
* Extracts the tensors of the image regions containing the detected faces.
* Useful if you want to compute the face descriptors for the face images.
......@@ -11,12 +13,12 @@ import { getImageTensor } from './getImageTensor';
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export function extractFaceTensors(image, detections) {
function extractFaceTensors(image, detections) {
return tf.tidy(function () {
var imgTensor = getImageTensor(image);
var imgTensor = getImageTensor_1.getImageTensor(image);
// TODO handle batches
var _a = imgTensor.shape, batchSize = _a[0], imgHeight = _a[1], imgWidth = _a[2], numChannels = _a[3];
var boxes = detections.map(function (det) { return det instanceof FaceDetection
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).getBox().floor()
: det; });
var faceTensors = boxes.map(function (_a) {
......@@ -26,4 +28,5 @@ export function extractFaceTensors(image, detections) {
return faceTensors;
});
}
exports.extractFaceTensors = extractFaceTensors;
//# sourceMappingURL=extractFaceTensors.js.map
\ No newline at end of file
dist/extractFaceTensors.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/extractFaces.js
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { createCanvas, getContext2dOrThrow } from './utils';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FaceDetection_1 = require("./faceDetectionNet/FaceDetection");
var utils_1 = require("./utils");
/**
* Extracts the image regions containing the detected faces.
*
......@@ -7,17 +9,18 @@ import { createCanvas, getContext2dOrThrow } from './utils';
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
export function extractFaces(image, detections) {
var ctx = getContext2dOrThrow(image);
var boxes = detections.map(function (det) { return det instanceof FaceDetection
function extractFaces(image, detections) {
var ctx = utils_1.getContext2dOrThrow(image);
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(image.width, image.height).getBox().floor()
: det; });
return boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = createCanvas({ width: width, height: height });
getContext2dOrThrow(faceImg)
var faceImg = utils_1.createCanvas({ width: width, height: height });
utils_1.getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg;
});
}
exports.extractFaces = extractFaces;
//# sourceMappingURL=extractFaces.js.map
\ No newline at end of file
dist/extractFaces.js.map
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dist/faceDetectionNet/FaceDetection.js
import { Rect } from '../Rect';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("../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);
this._box = new Rect_1.Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
}
FaceDetection.prototype.getScore = function () {
return this._score;
......@@ -20,12 +22,12 @@ var FaceDetection = /** @class */ (function () {
return this._imageHeight;
};
FaceDetection.prototype.getRelativeBox = function () {
return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
return new Rect_1.Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
};
FaceDetection.prototype.forSize = function (width, height) {
return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
};
return FaceDetection;
}());
export { FaceDetection };
exports.FaceDetection = FaceDetection;
//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
dist/faceDetectionNet/FaceDetection.js.map
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dist/faceDetectionNet/FaceDetectionNet.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { Rect } from '../Rect';
import { extractParams } from './extractParams';
import { FaceDetection } from './FaceDetection';
import { loadQuantizedParams } from './loadQuantizedParams';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { resizeLayer } from './resizeLayer';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var getImageTensor_1 = require("../getImageTensor");
var padToSquare_1 = require("../padToSquare");
var Rect_1 = require("../Rect");
var extractParams_1 = require("./extractParams");
var FaceDetection_1 = require("./FaceDetection");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var mobileNetV1_1 = require("./mobileNetV1");
var nonMaxSuppression_1 = require("./nonMaxSuppression");
var outputLayer_1 = require("./outputLayer");
var predictionLayer_1 = require("./predictionLayer");
var resizeLayer_1 = require("./resizeLayer");
var FaceDetectionNet = /** @class */ (function () {
function FaceDetectionNet() {
}
......@@ -28,7 +30,7 @@ var FaceDetectionNet = /** @class */ (function () {
throw new Error('FaceDetectionNet.load - expected model uri, or weights as Float32Array');
}
_a = this;
return [4 /*yield*/, loadQuantizedParams(weightsOrUrl)];
return [4 /*yield*/, loadQuantizedParams_1.loadQuantizedParams(weightsOrUrl)];
case 1:
_a._params = _b.sent();
return [2 /*return*/];
......@@ -37,7 +39,7 @@ var FaceDetectionNet = /** @class */ (function () {
});
};
FaceDetectionNet.prototype.extractWeights = function (weights) {
this._params = extractParams(weights);
this._params = extractParams_1.extractParams(weights);
};
FaceDetectionNet.prototype.forwardTensor = function (imgTensor) {
var _this = this;
......@@ -45,15 +47,15 @@ var FaceDetectionNet = /** @class */ (function () {
throw new Error('FaceDetectionNet - load model before inference');
}
return tf.tidy(function () {
var resized = resizeLayer(imgTensor);
var features = mobileNetV1(resized, _this._params.mobilenetv1_params);
var _a = predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params);
var resized = resizeLayer_1.resizeLayer(imgTensor);
var features = mobileNetV1_1.mobileNetV1(resized, _this._params.mobilenetv1_params);
var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
return outputLayer_1.outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params);
});
};
FaceDetectionNet.prototype.forward = function (input) {
var _this = this;
return tf.tidy(function () { return _this.forwardTensor(padToSquare(getImageTensor(input))); });
return tf.tidy(function () { return _this.forwardTensor(padToSquare_1.padToSquare(getImageTensor_1.getImageTensor(input))); });
};
FaceDetectionNet.prototype.locateFaces = function (input, minConfidence, maxResults) {
if (minConfidence === void 0) { minConfidence = 0.8; }
......@@ -66,10 +68,10 @@ var FaceDetectionNet = /** @class */ (function () {
case 0:
paddedHeightRelative = 1, paddedWidthRelative = 1;
_a = tf.tidy(function () {
var imgTensor = getImageTensor(input);
var imgTensor = getImageTensor_1.getImageTensor(input);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
imgTensor = padToSquare(imgTensor);
imgTensor = padToSquare_1.padToSquare(imgTensor);
paddedHeightRelative = imgTensor.shape[1] / height;
paddedWidthRelative = imgTensor.shape[2] / width;
return _this.forwardTensor(imgTensor);
......@@ -85,7 +87,7 @@ var FaceDetectionNet = /** @class */ (function () {
case 1:
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
results = indices
.map(function (idx) {
var _a = [
......@@ -96,7 +98,7 @@ var FaceDetectionNet = /** @class */ (function () {
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];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
return new FaceDetection_1.FaceDetection(scoresData[idx], new Rect_1.Rect(left, top, right - left, bottom - top), imageDimensions);
});
boxes.dispose();
scores.dispose();
......@@ -107,5 +109,5 @@ var FaceDetectionNet = /** @class */ (function () {
};
return FaceDetectionNet;
}());
export { FaceDetectionNet };
exports.FaceDetectionNet = FaceDetectionNet;
//# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file
dist/faceDetectionNet/FaceDetectionNet.js.map
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\ No newline at end of file
dist/faceDetectionNet/boxPredictionLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../commons/convLayer';
export function boxPredictionLayer(x, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var convLayer_1 = require("../commons/convLayer");
function boxPredictionLayer(x, params) {
return tf.tidy(function () {
var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convLayer(x, params.class_predictor_params), [batchSize, -1, 3]);
var boxPredictionEncoding = tf.reshape(convLayer_1.convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convLayer_1.convLayer(x, params.class_predictor_params), [batchSize, -1, 3]);
return {
boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction
};
});
}
exports.boxPredictionLayer = boxPredictionLayer;
//# sourceMappingURL=boxPredictionLayer.js.map
\ No newline at end of file
dist/faceDetectionNet/boxPredictionLayer.js.map
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\ No newline at end of file
{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/boxPredictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,kDAAiD;AAIjD,4BACE,CAAc,EACd,MAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,IAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,qBAAS,CAAC,CAAC,EAAE,MAAM,CAAC,6BAA6B,CAAC,EAClD,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,IAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,qBAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB,uBAAA;YACrB,eAAe,iBAAA;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AAtBD,gDAsBC"}
\ No newline at end of file
dist/faceDetectionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
function extractorsFactory(extractWeights) {
function extractDepthwiseConvParams(numChannels) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
......@@ -131,8 +133,8 @@ function extractorsFactory(extractWeights) {
extractPredictionLayerParams: extractPredictionLayerParams
};
}
export function extractParams(weights) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
function extractParams(weights) {
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1_params = extractMobilenetV1Params();
var prediction_layer_params = extractPredictionLayerParams();
......@@ -149,4 +151,5 @@ export function extractParams(weights) {
output_layer_params: output_layer_params
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
dist/faceDetectionNet/extractParams.js.map
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\ No newline at end of file
dist/faceDetectionNet/index.js
import { FaceDetectionNet } from './FaceDetectionNet';
export * from './FaceDetectionNet';
export function faceDetectionNet(weights) {
var net = new FaceDetectionNet();
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceDetectionNet_1 = require("./FaceDetectionNet");
tslib_1.__exportStar(require("./FaceDetectionNet"), exports);
function faceDetectionNet(weights) {
var net = new FaceDetectionNet_1.FaceDetectionNet();
net.extractWeights(weights);
return net;
}
exports.faceDetectionNet = faceDetectionNet;
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/faceDetectionNet/index.js.map
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dist/faceDetectionNet/loadQuantizedParams.js
import * as tslib_1 from "tslib";
import { isTensor1D, isTensor4D, isTensor3D } from '../commons/isTensor';
import { loadWeightMap } from '../commons/loadWeightMap';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var isTensor_1 = require("../commons/isTensor");
var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'face_detection_model';
function extractorsFactory(weightMap) {
function extractPointwiseConvParams(prefix, idx) {
......@@ -8,10 +10,10 @@ function extractorsFactory(weightMap) {
filters: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/weights"],
batch_norm_offset: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset"]
};
if (!isTensor4D(pointwise_conv_params.filters)) {
if (!isTensor_1.isTensor4D(pointwise_conv_params.filters)) {
throw new Error("expected weightMap[" + prefix + "/Conv2d_" + idx + "_pointwise/weights] to be a Tensor4D, instead have " + pointwise_conv_params.filters);
}
if (!isTensor1D(pointwise_conv_params.batch_norm_offset)) {
if (!isTensor_1.isTensor1D(pointwise_conv_params.batch_norm_offset)) {
throw new Error("expected weightMap[" + prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset] to be a Tensor1D, instead have " + pointwise_conv_params.batch_norm_offset);
}
return pointwise_conv_params;
......@@ -24,19 +26,19 @@ function extractorsFactory(weightMap) {
batch_norm_mean: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean"],
batch_norm_variance: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance"],
};
if (!isTensor4D(depthwise_conv_params.filters)) {
if (!isTensor_1.isTensor4D(depthwise_conv_params.filters)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights] to be a Tensor4D, instead have " + depthwise_conv_params.filters);
}
if (!isTensor1D(depthwise_conv_params.batch_norm_scale)) {
if (!isTensor_1.isTensor1D(depthwise_conv_params.batch_norm_scale)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_scale);
}
if (!isTensor1D(depthwise_conv_params.batch_norm_offset)) {
if (!isTensor_1.isTensor1D(depthwise_conv_params.batch_norm_offset)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_offset);
}
if (!isTensor1D(depthwise_conv_params.batch_norm_mean)) {
if (!isTensor_1.isTensor1D(depthwise_conv_params.batch_norm_mean)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_mean);
}
if (!isTensor1D(depthwise_conv_params.batch_norm_variance)) {
if (!isTensor_1.isTensor1D(depthwise_conv_params.batch_norm_variance)) {
throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_variance);
}
return {
......@@ -61,16 +63,16 @@ function extractorsFactory(weightMap) {
bias: weightMap["Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases"]
}
};
if (!isTensor4D(params.box_encoding_predictor_params.filters)) {
if (!isTensor_1.isTensor4D(params.box_encoding_predictor_params.filters)) {
throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights] to be a Tensor4D, instead have " + params.box_encoding_predictor_params.filters);
}
if (!isTensor1D(params.box_encoding_predictor_params.bias)) {
if (!isTensor_1.isTensor1D(params.box_encoding_predictor_params.bias)) {
throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/biases] to be a Tensor1D, instead have " + params.box_encoding_predictor_params.bias);
}
if (!isTensor4D(params.class_predictor_params.filters)) {
if (!isTensor_1.isTensor4D(params.class_predictor_params.filters)) {
throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/weights] to be a Tensor4D, instead have " + params.class_predictor_params.filters);
}
if (!isTensor1D(params.class_predictor_params.bias)) {
if (!isTensor_1.isTensor1D(params.class_predictor_params.bias)) {
throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases] to be a Tensor1D, instead have " + params.class_predictor_params.bias);
}
return params;
......@@ -98,17 +100,17 @@ function extractorsFactory(weightMap) {
extractPredictionLayerParams: extractPredictionLayerParams
};
}
export function loadQuantizedParams(uri) {
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
_a = extractorsFactory(weightMap), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
extra_dim = weightMap['Output/extra_dim'];
if (!isTensor3D(extra_dim)) {
if (!isTensor_1.isTensor3D(extra_dim)) {
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
}
return [2 /*return*/, {
......@@ -122,4 +124,5 @@ export function loadQuantizedParams(uri) {
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
dist/faceDetectionNet/loadQuantizedParams.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceDetectionNet/mobileNetV1.js
import * as tf from '@tensorflow/tfjs-core';
import { pointwiseConvLayer } from './pointwiseConvLayer';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
var epsilon = 0.0010000000474974513;
function depthwiseConvLayer(x, params, strides) {
return tf.tidy(function () {
......@@ -11,15 +13,15 @@ function depthwiseConvLayer(x, params, strides) {
function getStridesForLayerIdx(layerIdx) {
return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
}
export function mobileNetV1(x, params) {
function mobileNetV1(x, params) {
return tf.tidy(function () {
var conv11 = null;
var out = pointwiseConvLayer(x, params.conv_0_params, [2, 2]);
var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0_params, [2, 2]);
params.conv_pair_params.forEach(function (param, i) {
var layerIdx = i + 1;
var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
out = depthwiseConvLayer(out, param.depthwise_conv_params, depthwiseConvStrides);
out = pointwiseConvLayer(out, param.pointwise_conv_params, [1, 1]);
out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv_params, [1, 1]);
if (layerIdx === 11) {
conv11 = out;
}
......@@ -33,4 +35,5 @@ export function mobileNetV1(x, params) {
};
});
}
exports.mobileNetV1 = mobileNetV1;
//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file
dist/faceDetectionNet/mobileNetV1.js.map
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\ No newline at end of file
dist/faceDetectionNet/nonMaxSuppression.js
export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
var numBoxes = boxes.shape[0];
var outputSize = Math.min(maxOutputSize, numBoxes);
var candidates = scores
......@@ -28,6 +30,7 @@ export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, sc
});
return selected;
}
exports.nonMaxSuppression = nonMaxSuppression;
function IOU(boxes, i, j) {
var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
......
dist/faceDetectionNet/nonMaxSuppression.js.map
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\ No newline at end of file
dist/faceDetectionNet/outputLayer.js
import * as tf from '@tensorflow/tfjs-core';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function getCenterCoordinatesAndSizesLayer(x) {
var vec = tf.unstack(tf.transpose(x, [1, 0]));
var sizes = [
......@@ -28,7 +30,7 @@ function decodeBoxesLayer(x0, x1) {
tf.add(add1_out, div1_out)
]), [1, 0]);
}
export function outputLayer(boxPredictions, classPredictions, params) {
function outputLayer(boxPredictions, classPredictions, params) {
return tf.tidy(function () {
var batchSize = boxPredictions.shape[0];
var boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
......@@ -44,4 +46,5 @@ export function outputLayer(boxPredictions, classPredictions, params) {
};
});
}
exports.outputLayer = outputLayer;
//# sourceMappingURL=outputLayer.js.map
\ No newline at end of file
dist/faceDetectionNet/outputLayer.js.map
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\ No newline at end of file
dist/faceDetectionNet/pointwiseConvLayer.js
import * as tf from '@tensorflow/tfjs-core';
export function pointwiseConvLayer(x, params, strides) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function pointwiseConvLayer(x, params, strides) {
return tf.tidy(function () {
var out = tf.conv2d(x, params.filters, strides, 'same');
out = tf.add(out, params.batch_norm_offset);
return tf.clipByValue(out, 0, 6);
});
}
exports.pointwiseConvLayer = pointwiseConvLayer;
//# sourceMappingURL=pointwiseConvLayer.js.map
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dist/faceDetectionNet/pointwiseConvLayer.js.map
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dist/faceDetectionNet/predictionLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { boxPredictionLayer } from './boxPredictionLayer';
import { pointwiseConvLayer } from './pointwiseConvLayer';
export function predictionLayer(x, conv11, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var boxPredictionLayer_1 = require("./boxPredictionLayer");
var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
function predictionLayer(x, conv11, params) {
return tf.tidy(function () {
var conv0 = pointwiseConvLayer(x, params.conv_0_params, [1, 1]);
var conv1 = pointwiseConvLayer(conv0, params.conv_1_params, [2, 2]);
var conv2 = pointwiseConvLayer(conv1, params.conv_2_params, [1, 1]);
var conv3 = pointwiseConvLayer(conv2, params.conv_3_params, [2, 2]);
var conv4 = pointwiseConvLayer(conv3, params.conv_4_params, [1, 1]);
var conv5 = pointwiseConvLayer(conv4, params.conv_5_params, [2, 2]);
var conv6 = pointwiseConvLayer(conv5, params.conv_6_params, [1, 1]);
var conv7 = pointwiseConvLayer(conv6, params.conv_7_params, [2, 2]);
var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0_params);
var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1_params);
var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2_params);
var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3_params);
var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4_params);
var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5_params);
var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0_params, [1, 1]);
var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1_params, [2, 2]);
var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2_params, [1, 1]);
var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3_params, [2, 2]);
var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4_params, [1, 1]);
var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5_params, [2, 2]);
var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6_params, [1, 1]);
var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7_params, [2, 2]);
var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0_params);
var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1_params);
var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2_params);
var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3_params);
var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4_params);
var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5_params);
var boxPredictions = tf.concat([
boxPrediction0.boxPredictionEncoding,
boxPrediction1.boxPredictionEncoding,
......@@ -39,4 +41,5 @@ export function predictionLayer(x, conv11, params) {
};
});
}
exports.predictionLayer = predictionLayer;
//# sourceMappingURL=predictionLayer.js.map
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dist/faceDetectionNet/predictionLayer.js.map
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\ No newline at end of file
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dist/faceDetectionNet/resizeLayer.js
import * as tf from '@tensorflow/tfjs-core';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var resizedImageSize = [512, 512];
var weight = tf.scalar(0.007843137718737125);
var bias = tf.scalar(1);
export function resizeLayer(x) {
function resizeLayer(x) {
return tf.tidy(function () {
var resized = tf.image.resizeBilinear(x, resizedImageSize, false);
return tf.sub(tf.mul(resized, weight), bias);
});
}
exports.resizeLayer = resizeLayer;
//# sourceMappingURL=resizeLayer.js.map
\ No newline at end of file
dist/faceDetectionNet/resizeLayer.js.map
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\ No newline at end of file
dist/faceDetectionNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/faceLandmarkNet/FaceLandmarkNet.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../commons/convLayer';
import { getImageTensor } from '../getImageTensor';
import { Point } from '../Point';
import { extractParams } from './extractParams';
import { FaceLandmarks } from './FaceLandmarks';
import { fullyConnectedLayer } from './fullyConnectedLayer';
import { loadQuantizedParams } from './loadQuantizedParams';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var convLayer_1 = require("../commons/convLayer");
var getImageTensor_1 = require("../getImageTensor");
var Point_1 = require("../Point");
var extractParams_1 = require("./extractParams");
var FaceLandmarks_1 = require("./FaceLandmarks");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
function conv(x, params) {
return convLayer(x, params, 'valid', true);
return convLayer_1.convLayer(x, params, 'valid', true);
}
function maxPool(x, strides) {
if (strides === void 0) { strides = [2, 2]; }
......@@ -31,7 +33,7 @@ var FaceLandmarkNet = /** @class */ (function () {
throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array');
}
_a = this;
return [4 /*yield*/, loadQuantizedParams(weightsOrUrl)];
return [4 /*yield*/, loadQuantizedParams_1.loadQuantizedParams(weightsOrUrl)];
case 1:
_a._params = _b.sent();
return [2 /*return*/];
......@@ -40,7 +42,7 @@ var FaceLandmarkNet = /** @class */ (function () {
});
};
FaceLandmarkNet.prototype.extractWeights = function (weights) {
this._params = extractParams(weights);
this._params = extractParams_1.extractParams(weights);
};
FaceLandmarkNet.prototype.detectLandmarks = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
......@@ -54,7 +56,7 @@ var FaceLandmarkNet = /** @class */ (function () {
}
outTensor = tf.tidy(function () {
var params = _this._params;
var imgTensor = getImageTensor(input);
var imgTensor = getImageTensor_1.getImageTensor(input);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
// work with 128 x 128 sized face images
......@@ -73,8 +75,8 @@ var FaceLandmarkNet = /** @class */ (function () {
out = conv(out, params.conv6_params);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7_params);
var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1_params);
return fc1;
});
_b = (_a = Array).from;
......@@ -84,12 +86,12 @@ var FaceLandmarkNet = /** @class */ (function () {
outTensor.dispose();
xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; });
yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; });
return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), imageDimensions)];
return [2 /*return*/, new FaceLandmarks_1.FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point_1.Point(xCoords[i], yCoords[i]); }), imageDimensions)];
}
});
});
};
return FaceLandmarkNet;
}());
export { FaceLandmarkNet };
exports.FaceLandmarkNet = FaceLandmarkNet;
//# sourceMappingURL=FaceLandmarkNet.js.map
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dist/faceLandmarkNet/FaceLandmarkNet.js.map
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dist/faceLandmarkNet/FaceLandmarks.js
import { getCenterPoint } from '../commons/getCenterPoint';
import { FaceDetection } from '../faceDetectionNet/FaceDetection';
import { Point } from '../Point';
import { Rect } from '../Rect';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var getCenterPoint_1 = require("../commons/getCenterPoint");
var FaceDetection_1 = require("../faceDetectionNet/FaceDetection");
var Point_1 = require("../Point");
var Rect_1 = require("../Rect");
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
if (shift === void 0) { shift = new Point(0, 0); }
if (shift === void 0) { shift = new Point_1.Point(0, 0); }
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._shift = shift;
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point_1.Point(width, height)).add(shift); });
}
FaceLandmarks.prototype.getShift = function () {
return new Point(this._shift.x, this._shift.y);
return new Point_1.Point(this._shift.x, this._shift.y);
};
FaceLandmarks.prototype.getImageWidth = function () {
return this._imageWidth;
......@@ -29,7 +31,7 @@ var FaceLandmarks = /** @class */ (function () {
};
FaceLandmarks.prototype.getRelativePositions = function () {
var _this = this;
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this._imageWidth, _this._imageHeight)); });
return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point_1.Point(_this._imageWidth, _this._imageHeight)); });
};
FaceLandmarks.prototype.getJawOutline = function () {
return this._faceLandmarks.slice(0, 17);
......@@ -56,7 +58,7 @@ var FaceLandmarks = /** @class */ (function () {
return new FaceLandmarks(this.getRelativePositions(), { width: width, height: height });
};
FaceLandmarks.prototype.shift = function (x, y) {
return new FaceLandmarks(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point(x, y));
return new FaceLandmarks(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point_1.Point(x, y));
};
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
......@@ -71,7 +73,7 @@ var FaceLandmarks = /** @class */ (function () {
*/
FaceLandmarks.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection
var box = detection instanceof FaceDetection_1.FaceDetection
? detection.getBox().floor()
: detection;
return this.shift(box.x, box.y).align();
......@@ -80,18 +82,18 @@ var FaceLandmarks = /** @class */ (function () {
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(getCenterPoint);
].map(getCenterPoint_1.getCenterPoint);
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = getCenterPoint(centers);
var refPoint = getCenterPoint_1.getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new Rect(x, y, size, size);
return new Rect_1.Rect(x, y, size, size);
};
return FaceLandmarks;
}());
export { FaceLandmarks };
exports.FaceLandmarks = FaceLandmarks;
//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
dist/faceLandmarkNet/FaceLandmarks.js.map
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\ No newline at end of file
dist/faceLandmarkNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractConvParamsFactory } from '../commons/extractConvParamsFactory';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
export function extractParams(weights) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = extractConvParamsFactory(extractWeights);
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
function extractParams(weights) {
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights);
function extractFcParams(channelsIn, channelsOut) {
var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var fc_bias = tf.tensor1d(extractWeights(channelsOut));
......@@ -38,4 +40,5 @@ export function extractParams(weights) {
fc1_params: fc1_params
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
dist/faceLandmarkNet/extractParams.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceLandmarkNet/fullyConnectedLayer.js
import * as tf from '@tensorflow/tfjs-core';
export function fullyConnectedLayer(x, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function fullyConnectedLayer(x, params) {
return tf.tidy(function () {
return tf.add(tf.matMul(x, params.weights), params.bias);
});
}
exports.fullyConnectedLayer = fullyConnectedLayer;
//# sourceMappingURL=fullyConnectedLayer.js.map
\ No newline at end of file
dist/faceLandmarkNet/fullyConnectedLayer.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceLandmarkNet/index.js
import { FaceLandmarkNet } from './FaceLandmarkNet';
export * from './FaceLandmarkNet';
export function faceLandmarkNet(weights) {
var net = new FaceLandmarkNet();
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceLandmarkNet_1 = require("./FaceLandmarkNet");
tslib_1.__exportStar(require("./FaceLandmarkNet"), exports);
function faceLandmarkNet(weights) {
var net = new FaceLandmarkNet_1.FaceLandmarkNet();
net.extractWeights(weights);
return net;
}
exports.faceLandmarkNet = faceLandmarkNet;
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/faceLandmarkNet/index.js.map
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\ No newline at end of file
dist/faceLandmarkNet/loadQuantizedParams.js
import * as tslib_1 from "tslib";
import { loadWeightMap } from '../commons/loadWeightMap';
import { isTensor4D, isTensor1D, isTensor2D } from '../commons/isTensor';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var loadWeightMap_1 = require("../commons/loadWeightMap");
var isTensor_1 = require("../commons/isTensor");
var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
function extractorsFactory(weightMap) {
function extractConvParams(prefix) {
......@@ -8,10 +10,10 @@ function extractorsFactory(weightMap) {
filters: weightMap[prefix + "/kernel"],
bias: weightMap[prefix + "/bias"]
};
if (!isTensor4D(params.filters)) {
if (!isTensor_1.isTensor4D(params.filters)) {
throw new Error("expected weightMap[" + prefix + "/kernel] to be a Tensor4D, instead have " + params.filters);
}
if (!isTensor1D(params.bias)) {
if (!isTensor_1.isTensor1D(params.bias)) {
throw new Error("expected weightMap[" + prefix + "/bias] to be a Tensor1D, instead have " + params.bias);
}
return params;
......@@ -21,10 +23,10 @@ function extractorsFactory(weightMap) {
weights: weightMap[prefix + "/kernel"],
bias: weightMap[prefix + "/bias"]
};
if (!isTensor2D(params.weights)) {
if (!isTensor_1.isTensor2D(params.weights)) {
throw new Error("expected weightMap[" + prefix + "/kernel] to be a Tensor2D, instead have " + params.weights);
}
if (!isTensor1D(params.bias)) {
if (!isTensor_1.isTensor1D(params.bias)) {
throw new Error("expected weightMap[" + prefix + "/bias] to be a Tensor1D, instead have " + params.bias);
}
return params;
......@@ -34,12 +36,12 @@ function extractorsFactory(weightMap) {
extractFcParams: extractFcParams
};
}
export function loadQuantizedParams(uri) {
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, _a, extractConvParams, extractFcParams;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
_a = extractorsFactory(weightMap), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams;
......@@ -59,4 +61,5 @@ export function loadQuantizedParams(uri) {
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
dist/faceLandmarkNet/loadQuantizedParams.js.map
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\ No newline at end of file
dist/faceLandmarkNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/faceRecognitionNet/FaceRecognitionNet.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { convDown } from './convLayer';
import { extractParams } from './extractParams';
import { loadQuantizedParams } from './loadQuantizedParams';
import { normalize } from './normalize';
import { residual, residualDown } from './residualLayer';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var getImageTensor_1 = require("../getImageTensor");
var padToSquare_1 = require("../padToSquare");
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 () {
function FaceRecognitionNet() {
}
......@@ -24,7 +26,7 @@ var FaceRecognitionNet = /** @class */ (function () {
throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array');
}
_a = this;
return [4 /*yield*/, loadQuantizedParams(weightsOrUrl)];
return [4 /*yield*/, loadQuantizedParams_1.loadQuantizedParams(weightsOrUrl)];
case 1:
_a._params = _b.sent();
return [2 /*return*/];
......@@ -33,7 +35,7 @@ var FaceRecognitionNet = /** @class */ (function () {
});
};
FaceRecognitionNet.prototype.extractWeights = function (weights) {
this._params = extractParams(weights);
this._params = extractParams_1.extractParams(weights);
};
FaceRecognitionNet.prototype.forward = function (input) {
var _this = this;
......@@ -41,28 +43,28 @@ var FaceRecognitionNet = /** @class */ (function () {
throw new Error('FaceRecognitionNet - load model before inference');
}
return tf.tidy(function () {
var x = padToSquare(getImageTensor(input), true);
var x = padToSquare_1.padToSquare(getImageTensor_1.getImageTensor(input), true);
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150]);
}
x = normalize(x);
var out = convDown(x, _this._params.conv32_down);
x = normalize_1.normalize(x);
var out = convLayer_1.convDown(x, _this._params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residual(out, _this._params.conv32_1);
out = residual(out, _this._params.conv32_2);
out = residual(out, _this._params.conv32_3);
out = residualDown(out, _this._params.conv64_down);
out = residual(out, _this._params.conv64_1);
out = residual(out, _this._params.conv64_2);
out = residual(out, _this._params.conv64_3);
out = residualDown(out, _this._params.conv128_down);
out = residual(out, _this._params.conv128_1);
out = residual(out, _this._params.conv128_2);
out = residualDown(out, _this._params.conv256_down);
out = residual(out, _this._params.conv256_1);
out = residual(out, _this._params.conv256_2);
out = residualDown(out, _this._params.conv256_down_out);
out = residualLayer_1.residual(out, _this._params.conv32_1);
out = residualLayer_1.residual(out, _this._params.conv32_2);
out = residualLayer_1.residual(out, _this._params.conv32_3);
out = residualLayer_1.residualDown(out, _this._params.conv64_down);
out = residualLayer_1.residual(out, _this._params.conv64_1);
out = residualLayer_1.residual(out, _this._params.conv64_2);
out = residualLayer_1.residual(out, _this._params.conv64_3);
out = residualLayer_1.residualDown(out, _this._params.conv128_down);
out = residualLayer_1.residual(out, _this._params.conv128_1);
out = residualLayer_1.residual(out, _this._params.conv128_2);
out = residualLayer_1.residualDown(out, _this._params.conv256_down);
out = residualLayer_1.residual(out, _this._params.conv256_1);
out = residualLayer_1.residual(out, _this._params.conv256_2);
out = residualLayer_1.residualDown(out, _this._params.conv256_down_out);
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
return fullyConnected;
......@@ -97,5 +99,5 @@ var FaceRecognitionNet = /** @class */ (function () {
};
return FaceRecognitionNet;
}());
export { FaceRecognitionNet };
exports.FaceRecognitionNet = FaceRecognitionNet;
//# sourceMappingURL=FaceRecognitionNet.js.map
\ No newline at end of file
dist/faceRecognitionNet/FaceRecognitionNet.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceRecognitionNet/convLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { scale } from './scaleLayer';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var scaleLayer_1 = require("./scaleLayer");
function convLayer(x, params, strides, withRelu, padding) {
if (padding === void 0) { padding = 'same'; }
var _a = params.conv, filters = _a.filters, bias = _a.bias;
var out = tf.conv2d(x, filters, strides, padding);
out = tf.add(out, bias);
out = scale(out, params.scale);
out = scaleLayer_1.scale(out, params.scale);
return withRelu ? tf.relu(out) : out;
}
export function conv(x, params) {
function conv(x, params) {
return convLayer(x, params, [1, 1], true);
}
export function convNoRelu(x, params) {
exports.conv = conv;
function convNoRelu(x, params) {
return convLayer(x, params, [1, 1], false);
}
export function convDown(x, params) {
exports.convNoRelu = convNoRelu;
function convDown(x, params) {
return convLayer(x, params, [2, 2], true, 'valid');
}
exports.convDown = convDown;
//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
dist/faceRecognitionNet/convLayer.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceRecognitionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { isFloat } from '../utils';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
var utils_1 = require("../utils");
function extractorsFactory(extractWeights) {
function extractFilterValues(numFilterValues, numFilters, filterSize) {
var weights = extractWeights(numFilterValues);
var depth = weights.length / (numFilters * filterSize * filterSize);
if (isFloat(depth)) {
if (utils_1.isFloat(depth)) {
throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
}
return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]);
......@@ -44,8 +46,8 @@ function extractorsFactory(extractWeights) {
extractResidualLayerParams: extractResidualLayerParams
};
}
export function extractParams(weights) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
function extractParams(weights) {
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
var conv32_down = extractConvLayerParams(4704, 32, 7);
var conv32_1 = extractResidualLayerParams(9216, 32, 3);
......@@ -85,4 +87,5 @@ export function extractParams(weights) {
fc: fc
};
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
dist/faceRecognitionNet/extractParams.js.map
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\ No newline at end of file
dist/faceRecognitionNet/index.js
import { FaceRecognitionNet } from './FaceRecognitionNet';
export * from './FaceRecognitionNet';
export function faceRecognitionNet(weights) {
var net = new FaceRecognitionNet();
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceRecognitionNet_1 = require("./FaceRecognitionNet");
tslib_1.__exportStar(require("./FaceRecognitionNet"), exports);
function faceRecognitionNet(weights) {
var net = new FaceRecognitionNet_1.FaceRecognitionNet();
net.extractWeights(weights);
return net;
}
exports.faceRecognitionNet = faceRecognitionNet;
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/faceRecognitionNet/index.js.map
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\ No newline at end of file
dist/faceRecognitionNet/loadQuantizedParams.js
import * as tslib_1 from "tslib";
import { isTensor1D, isTensor2D, isTensor4D } from '../commons/isTensor';
import { loadWeightMap } from '../commons/loadWeightMap';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var isTensor_1 = require("../commons/isTensor");
var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'face_recognition_model';
function extractorsFactory(weightMap) {
function extractScaleLayerParams(prefix) {
......@@ -8,10 +10,10 @@ function extractorsFactory(weightMap) {
weights: weightMap[prefix + "/scale/weights"],
biases: weightMap[prefix + "/scale/biases"]
};
if (!isTensor1D(params.weights)) {
if (!isTensor_1.isTensor1D(params.weights)) {
throw new Error("expected weightMap[" + prefix + "/scale/weights] to be a Tensor1D, instead have " + params.weights);
}
if (!isTensor1D(params.biases)) {
if (!isTensor_1.isTensor1D(params.biases)) {
throw new Error("expected weightMap[" + prefix + "/scale/biases] to be a Tensor1D, instead have " + params.biases);
}
return params;
......@@ -21,10 +23,10 @@ function extractorsFactory(weightMap) {
filters: weightMap[prefix + "/conv/filters"],
bias: weightMap[prefix + "/conv/bias"]
};
if (!isTensor4D(params.filters)) {
if (!isTensor_1.isTensor4D(params.filters)) {
throw new Error("expected weightMap[" + prefix + "/conv/filters] to be a Tensor1D, instead have " + params.filters);
}
if (!isTensor1D(params.bias)) {
if (!isTensor_1.isTensor1D(params.bias)) {
throw new Error("expected weightMap[" + prefix + "/conv/bias] to be a Tensor1D, instead have " + params.bias);
}
return {
......@@ -43,12 +45,12 @@ function extractorsFactory(weightMap) {
extractResidualLayerParams: extractResidualLayerParams
};
}
export function loadQuantizedParams(uri) {
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, _a, extractConvLayerParams, extractResidualLayerParams, conv32_down, conv32_1, conv32_2, conv32_3, conv64_down, conv64_1, conv64_2, conv64_3, conv128_down, conv128_1, conv128_2, conv256_down, conv256_1, conv256_2, conv256_down_out, fc;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
_a = extractorsFactory(weightMap), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
......@@ -68,7 +70,7 @@ export function loadQuantizedParams(uri) {
conv256_2 = extractResidualLayerParams('conv256_2');
conv256_down_out = extractResidualLayerParams('conv256_down_out');
fc = weightMap['fc'];
if (!isTensor2D(fc)) {
if (!isTensor_1.isTensor2D(fc)) {
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
}
return [2 /*return*/, {
......@@ -93,4 +95,5 @@ export function loadQuantizedParams(uri) {
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
dist/faceRecognitionNet/loadQuantizedParams.js.map
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\ No newline at end of file
dist/faceRecognitionNet/normalize.js
import * as tf from '@tensorflow/tfjs-core';
export function normalize(x) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function normalize(x) {
return tf.tidy(function () {
var avg_r = tf.fill([1, 150, 150, 1], 122.782);
var avg_g = tf.fill([1, 150, 150, 1], 117.001);
......@@ -8,4 +10,5 @@ export function normalize(x) {
return tf.div(tf.sub(x, avg_rgb), tf.scalar(256));
});
}
exports.normalize = normalize;
//# sourceMappingURL=normalize.js.map
\ No newline at end of file
dist/faceRecognitionNet/normalize.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceRecognitionNet/residualLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { conv, convDown, convNoRelu } from './convLayer';
export function residual(x, params) {
var out = conv(x, params.conv1);
out = convNoRelu(out, params.conv2);
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var convLayer_1 = require("./convLayer");
function residual(x, params) {
var out = convLayer_1.conv(x, params.conv1);
out = convLayer_1.convNoRelu(out, params.conv2);
out = tf.add(out, x);
out = tf.relu(out);
return out;
}
export function residualDown(x, params) {
var out = convDown(x, params.conv1);
out = convNoRelu(out, params.conv2);
exports.residual = residual;
function residualDown(x, params) {
var out = convLayer_1.convDown(x, params.conv1);
out = convLayer_1.convNoRelu(out, params.conv2);
var pooled = tf.avgPool(x, 2, 2, 'valid');
var zeros = tf.zeros(pooled.shape);
var isPad = pooled.shape[3] !== out.shape[3];
......@@ -29,4 +32,5 @@ export function residualDown(x, params) {
out = tf.relu(out);
return out;
}
exports.residualDown = residualDown;
//# sourceMappingURL=residualLayer.js.map
\ No newline at end of file
dist/faceRecognitionNet/residualLayer.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceRecognitionNet/scaleLayer.js
import * as tf from '@tensorflow/tfjs-core';
export function scale(x, params) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function scale(x, params) {
return tf.add(tf.mul(x, params.weights), params.biases);
}
exports.scale = scale;
//# sourceMappingURL=scaleLayer.js.map
\ No newline at end of file
dist/faceRecognitionNet/scaleLayer.js.map
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\ No newline at end of file
dist/faceRecognitionNet/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/getImageTensor.js
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from './NetInput';
export function getImageTensor(input) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var NetInput_1 = require("./NetInput");
function getImageTensor(input) {
return tf.tidy(function () {
if (input instanceof tf.Tensor) {
var rank = input.shape.length;
......@@ -9,10 +11,11 @@ export function getImageTensor(input) {
}
return (rank === 3 ? input.expandDims(0) : input).toFloat();
}
var netInput = input instanceof NetInput ? input : new NetInput(input);
var netInput = input instanceof NetInput_1.NetInput ? input : new NetInput_1.NetInput(input);
return tf.concat(netInput.canvases.map(function (canvas) {
return tf.fromPixels(canvas).expandDims(0).toFloat();
}));
});
}
exports.getImageTensor = getImageTensor;
//# sourceMappingURL=getImageTensor.js.map
\ No newline at end of file
dist/getImageTensor.js.map
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\ No newline at end of file
dist/index.js
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export { euclideanDistance, NetInput, tf, padToSquare };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './faceDetectionNet';
export * from './faceLandmarkNet';
export * from './faceRecognitionNet';
export * from './utils';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
exports.tf = tf;
var euclideanDistance_1 = require("./euclideanDistance");
exports.euclideanDistance = euclideanDistance_1.euclideanDistance;
var NetInput_1 = require("./NetInput");
exports.NetInput = NetInput_1.NetInput;
var padToSquare_1 = require("./padToSquare");
exports.padToSquare = padToSquare_1.padToSquare;
tslib_1.__exportStar(require("./extractFaces"), exports);
tslib_1.__exportStar(require("./extractFaceTensors"), exports);
tslib_1.__exportStar(require("./faceDetectionNet"), exports);
tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
tslib_1.__exportStar(require("./utils"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/index.js.map
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\ No newline at end of file
dist/padToSquare.js
import * as tf from '@tensorflow/tfjs-core';
import { isEven } from './utils';
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var utils_1 = require("./utils");
/**
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
......@@ -7,7 +9,7 @@ import { isEven } from './utils';
* @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.
*/
export function padToSquare(imgTensor, isCenterImage) {
function padToSquare(imgTensor, isCenterImage) {
if (isCenterImage === void 0) { isCenterImage = false; }
return tf.tidy(function () {
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
......@@ -23,9 +25,10 @@ export function padToSquare(imgTensor, isCenterImage) {
paddingTensorShape[paddingAxis] = paddingAmount + (isRoundUp ? 1 : 0);
return paddingTensorShape;
};
var tensorsToStack = (isCenterImage ? [tf.fill(getPaddingTensorShape(!isEven(dimDiff)), 0)] : [])
var tensorsToStack = (isCenterImage ? [tf.fill(getPaddingTensorShape(!utils_1.isEven(dimDiff)), 0)] : [])
.concat([imgTensor, tf.fill(getPaddingTensorShape(), 0)]);
return tf.concat(tensorsToStack, paddingAxis);
});
}
exports.padToSquare = padToSquare;
//# sourceMappingURL=padToSquare.js.map
\ No newline at end of file
dist/padToSquare.js.map
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\ No newline at end of file
dist/types.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/utils.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
export function isFloat(num) {
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
function isFloat(num) {
return num % 1 !== 0;
}
export function isEven(num) {
exports.isFloat = isFloat;
function isEven(num) {
return num % 2 === 0;
}
export function round(num) {
exports.isEven = isEven;
function round(num) {
return Math.floor(num * 100) / 100;
}
export function getElement(arg) {
exports.round = round;
function getElement(arg) {
if (typeof arg === 'string') {
return document.getElementById(arg);
}
return arg;
}
export function getContext2dOrThrow(canvas) {
exports.getElement = getElement;
function getContext2dOrThrow(canvas) {
var ctx = canvas.getContext('2d');
if (!ctx) {
throw new Error('canvas 2d context is null');
}
return ctx;
}
export function createCanvas(_a) {
exports.getContext2dOrThrow = getContext2dOrThrow;
function createCanvas(_a) {
var width = _a.width, height = _a.height;
var canvas = document.createElement('canvas');
canvas.width = width;
canvas.height = height;
return canvas;
}
export function createCanvasFromMedia(media, dims) {
exports.createCanvas = createCanvas;
function createCanvasFromMedia(media, dims) {
var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
var canvas = createCanvas({ width: width, height: height });
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
return canvas;
}
export function getMediaDimensions(media) {
exports.createCanvasFromMedia = createCanvasFromMedia;
function getMediaDimensions(media) {
if (media instanceof HTMLImageElement) {
return { width: media.naturalWidth, height: media.naturalHeight };
}
......@@ -44,7 +53,8 @@ export function getMediaDimensions(media) {
}
return media;
}
export function bufferToImage(buf) {
exports.getMediaDimensions = getMediaDimensions;
function bufferToImage(buf) {
return new Promise(function (resolve, reject) {
if (!(buf instanceof Blob)) {
return reject('bufferToImage - expected buf to be of type: Blob');
......@@ -60,7 +70,8 @@ export function bufferToImage(buf) {
reader.readAsDataURL(buf);
});
}
export function imageTensorToCanvas(imgTensor, canvas) {
exports.bufferToImage = bufferToImage;
function imageTensorToCanvas(imgTensor, canvas) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var targetCanvas, _a, _, height, width, numChannels;
return tslib_1.__generator(this, function (_b) {
......@@ -76,7 +87,8 @@ export function imageTensorToCanvas(imgTensor, canvas) {
});
});
}
export function getDefaultDrawOptions() {
exports.imageTensorToCanvas = imageTensorToCanvas;
function getDefaultDrawOptions() {
return {
color: 'blue',
lineWidth: 2,
......@@ -84,20 +96,23 @@ export function getDefaultDrawOptions() {
fontStyle: 'Georgia'
};
}
export function drawBox(ctx, x, y, w, h, options) {
exports.getDefaultDrawOptions = getDefaultDrawOptions;
function drawBox(ctx, x, y, w, h, options) {
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
ctx.strokeStyle = drawOptions.color;
ctx.lineWidth = drawOptions.lineWidth;
ctx.strokeRect(x, y, w, h);
}
export function drawText(ctx, x, y, text, options) {
exports.drawBox = drawBox;
function drawText(ctx, x, y, text, options) {
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var padText = 2 + drawOptions.lineWidth;
ctx.fillStyle = drawOptions.color;
ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
}
export function drawDetection(canvasArg, detection, options) {
exports.drawText = drawText;
function drawDetection(canvasArg, detection, options) {
var canvas = getElement(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement');
......@@ -116,6 +131,7 @@ export function drawDetection(canvasArg, detection, options) {
}
});
}
exports.drawDetection = drawDetection;
function drawContour(ctx, points, isClosed) {
if (isClosed === void 0) { isClosed = false; }
ctx.beginPath();
......@@ -136,7 +152,7 @@ function drawContour(ctx, points, isClosed) {
}
ctx.stroke();
}
export function drawLandmarks(canvasArg, faceLandmarks, options) {
function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = getElement(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
......@@ -162,4 +178,5 @@ export function drawLandmarks(canvasArg, faceLandmarks, options) {
ctx.fillStyle = color;
faceLandmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
}
exports.drawLandmarks = drawLandmarks;
//# sourceMappingURL=utils.js.map
\ No newline at end of file
rollup.config.js
......@@ -9,7 +9,13 @@ const { minify } = process.env
export default {
input: 'src/index.ts',
plugins: [
typescript(),
typescript({
tsconfigOverride: {
compilerOptions: {
module: 'ES2015'
}
}
}),
node(),
commonjs()
].concat(minify ? uglify() : []),
......
tsconfig.json
......@@ -18,7 +18,7 @@
"suppressImplicitAnyIndexErrors": true,
"strictNullChecks": true,
"importHelpers": true,
"module": "ES2015",
"module": "commonjs",
"moduleResolution": "node",
"target": "es5",
"outDir": "dist",
......
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