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Use published blazeface-back model

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This commit is contained in:
Shailesh Pandit 2022-01-24 10:53:37 +05:30
parent 868d19b3d0
commit aab8351b23
26 changed files with 8 additions and 1524 deletions
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2
package.json
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@ -17,7 +17,6 @@
"@ffmpeg/ffmpeg": "^0.10.1",
"@sentry/nextjs": "^6.7.1",
"@stripe/stripe-js": "^1.13.2",
"@tensorflow-models/blazeface": "./thirdparty/blazeface",
"@tensorflow/tfjs": "^3.11.0",
"@tensorflow/tfjs-backend-wasm": "^3.11.0",
"@tensorflow/tfjs-backend-webgl": "^3.11.0",
@ -28,6 +27,7 @@
"@typescript-eslint/parser": "^4.25.0",
"axios": "^0.21.3",
"bip39": "^3.0.4",
"blazeface-back": "^0.0.8",
"bootstrap": "^4.5.2",
"chrono-node": "^2.2.6",
"comlink": "^4.3.0",

2
src/services/machineLearning/blazeFaceDetectionService.ts
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@ -2,7 +2,7 @@ import {
load as blazeFaceLoad,
BlazeFaceModel,
NormalizedFace,
} from '@tensorflow-models/blazeface';
} from 'blazeface-back';
import * as tf from '@tensorflow/tfjs-core';
import { GraphModel } from '@tensorflow/tfjs';
import {

2
src/utils/machineLearning/index.ts
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@ -1,4 +1,4 @@
import { NormalizedFace } from '@tensorflow-models/blazeface';
import { NormalizedFace } from 'blazeface-back';
import * as tf from '@tensorflow/tfjs-core';
import { ML_SYNC_DOWNLOAD_TIMEOUT_MS } from 'constants/machineLearning/config';
import { euclidean } from 'hdbscan';

17
thirdparty/blazeface/dist/blazeface.esm.js vendored
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557
thirdparty/blazeface/dist/blazeface.js vendored
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@ -1,557 +0,0 @@
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
(function (global, factory) {
typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core'), require('@tensorflow/tfjs-converter')) :
typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core', '@tensorflow/tfjs-converter'], factory) :
(factory((global.blazeface = {}),global.tf,global.tf));
}(this, (function (exports,tf,tfconv) { 'use strict';
/*! *****************************************************************************
Copyright (c) Microsoft Corporation.
Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted.
THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
PERFORMANCE OF THIS SOFTWARE.
***************************************************************************** */
function __awaiter(thisArg, _arguments, P, generator) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
}
function __generator(thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
}
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var disposeBox = function (box) {
box.startEndTensor.dispose();
box.startPoint.dispose();
box.endPoint.dispose();
};
var createBox = function (startEndTensor) { return ({
startEndTensor: startEndTensor,
startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),
endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2])
}); };
var scaleBox = function (box, factors) {
var starts = tf.mul(box.startPoint, factors);
var ends = tf.mul(box.endPoint, factors);
var newCoordinates = tf.concat2d([starts, ends], 1);
return createBox(newCoordinates);
};
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// const ANCHORS_CONFIG: AnchorsConfig = {
// 'strides': [8, 16],
// 'anchors': [2, 6]
// };
// `NUM_LANDMARKS` is a fixed property of the model.
var NUM_LANDMARKS = 6;
function generateAnchors(width, height, outputSpec) {
var anchors = [];
for (var i = 0; i < outputSpec.strides.length; i++) {
var stride = outputSpec.strides[i];
var gridRows = Math.floor((height + stride - 1) / stride);
var gridCols = Math.floor((width + stride - 1) / stride);
var anchorsNum = outputSpec.anchors[i];
for (var gridY = 0; gridY < gridRows; gridY++) {
var anchorY = stride * (gridY + 0.5);
for (var gridX = 0; gridX < gridCols; gridX++) {
var anchorX = stride * (gridX + 0.5);
for (var n = 0; n < anchorsNum; n++) {
anchors.push([anchorX, anchorY]);
}
}
}
}
return anchors;
}
function decodeBounds(boxOutputs, anchors, inputSize) {
var boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);
var centers = tf.add(boxStarts, anchors);
var boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);
var boxSizesNormalized = tf.div(boxSizes, inputSize);
var centersNormalized = tf.div(centers, inputSize);
var halfBoxSize = tf.div(boxSizesNormalized, 2);
var starts = tf.sub(centersNormalized, halfBoxSize);
var ends = tf.add(centersNormalized, halfBoxSize);
var startNormalized = tf.mul(starts, inputSize);
var endNormalized = tf.mul(ends, inputSize);
var concatAxis = 1;
return tf.concat2d([startNormalized, endNormalized], concatAxis);
}
function getInputTensorDimensions(input) {
return input instanceof tf.Tensor ? [input.shape[0], input.shape[1]] :
[input.height, input.width];
}
function flipFaceHorizontal(face, imageWidth) {
var flippedTopLeft, flippedBottomRight, flippedLandmarks;
if (face.topLeft instanceof tf.Tensor &&
face.bottomRight instanceof tf.Tensor) {
var _a = tf.tidy(function () {
return [
tf.concat([
tf.slice(tf.sub(imageWidth - 1, face.topLeft), 0, 1),
tf.slice(face.topLeft, 1, 1)
]),
tf.concat([
tf.sub(imageWidth - 1, tf.slice(face.bottomRight, 0, 1)),
tf.slice(face.bottomRight, 1, 1)
])
];
}), topLeft = _a[0], bottomRight = _a[1];
flippedTopLeft = topLeft;
flippedBottomRight = bottomRight;
if (face.landmarks != null) {
flippedLandmarks = tf.tidy(function () {
var a = tf.sub(tf.tensor1d([imageWidth - 1, 0]), face.landmarks);
var b = tf.tensor1d([1, -1]);
var product = tf.mul(a, b);
return product;
});
}
}
else {
var _b = face.topLeft, topLeftX = _b[0], topLeftY = _b[1];
var _c = face.bottomRight, bottomRightX = _c[0], bottomRightY = _c[1];
flippedTopLeft = [imageWidth - 1 - topLeftX, topLeftY];
flippedBottomRight = [imageWidth - 1 - bottomRightX, bottomRightY];
if (face.landmarks != null) {
flippedLandmarks =
face.landmarks.map(function (coord) { return ([
imageWidth - 1 - coord[0],
coord[1]
]); });
}
}
var flippedFace = {
topLeft: flippedTopLeft,
bottomRight: flippedBottomRight
};
if (flippedLandmarks != null) {
flippedFace.landmarks = flippedLandmarks;
}
if (face.probability != null) {
flippedFace.probability = face.probability instanceof tf.Tensor ?
face.probability.clone() :
face.probability;
}
return flippedFace;
}
function scaleBoxFromPrediction(face, scaleFactor) {
return tf.tidy(function () {
var box;
if (face.hasOwnProperty('box')) {
box = face.box;
}
else {
box = face;
}
return tf.squeeze(scaleBox(box, scaleFactor).startEndTensor);
});
}
var BlazeFaceModel = /** @class */ (function () {
function BlazeFaceModel(model, width, height, maxFaces, iouThreshold, scoreThreshold) {
this.blazeFaceModel = model;
this.width = width;
this.height = height;
this.maxFaces = maxFaces;
var outputSpec = { strides: [width / 16, width / 8], anchors: [2, 6] };
this.anchorsData = generateAnchors(width, height, outputSpec);
this.anchors = tf.tensor2d(this.anchorsData);
// this.inputSizeData = [width, height];
this.inputSize = tf.tensor1d([width, height]);
this.iouThreshold = iouThreshold;
this.scoreThreshold = scoreThreshold;
}
BlazeFaceModel.prototype.resizeAspectRatio = function (inputImage, width, height) {
var imgWidth = inputImage.shape[2];
var imgHeight = inputImage.shape[1];
// console.log("img dim", imgWidth, imgHeight, width, height)
if (!width || !height) {
return {
ratio: 1,
image: inputImage
};
}
var ratio = Math.min(width / imgWidth, height / imgHeight);
var dimWidth = Math.round(imgWidth * ratio);
var dimHeight = Math.round(imgHeight * ratio);
// console.log("resizing to:", dimWidth, dimHeight, ratio);
return {
ratio: ratio,
image: tf.image.resizeBilinear(inputImage, [dimHeight, dimWidth])
};
};
BlazeFaceModel.prototype.getBoundingBoxes = function (inputImage, returnTensors, annotateBoxes) {
if (annotateBoxes === void 0) { annotateBoxes = true; }
return __awaiter(this, void 0, void 0, function () {
var _a, detectedOutputs, boxes, scores, ratio, savedConsoleWarnFn, boxIndicesTensor, boxIndices, boundingBoxes, scaleFactor, annotatedBoxes, _loop_1, i;
var _this = this;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = tf.tidy(function () {
var resizedImage = tf.image.resizeBilinear(inputImage, [_this.width, _this.height]);
// const resized = this.resizeAspectRatio(inputImage, this.width, this.height);
// const transform = tf.tensor2d([[1,0,0,0,1,0,0,0]]);
// const paddedImage = tf.image.transform(resized.image, transform, 'nearest', 'constant', 0, [this.width, this.height]);
var normalizedImage = tf.mul(tf.sub(tf.div(resizedImage, 255), 0.5), 2);
// [1, 897, 17] 1 = batch, 897 = number of anchors
var batchedPrediction = _this.blazeFaceModel.predict(normalizedImage);
// console.log(batchedPrediction);
var batchPred = batchedPrediction;
var sorted = batchPred.sort(function (a, b) { return a.size - b.size; });
// console.log("sorted: ", sorted);
var concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16
// console.log(concat384);
var concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16
// console.log(concat512);
var concat = tf.concat([concat512, concat384], 1);
// console.log(concat);
var prediction = tf.squeeze(concat);
// console.log(prediction);
// const prediction = tf.squeeze((batchedPrediction as tf.Tensor3D));
var decodedBounds = decodeBounds(prediction, _this.anchors, _this.inputSize);
var logits = tf.slice(prediction, [0, 0], [-1, 1]);
var scores = tf.squeeze(tf.sigmoid(logits));
return [prediction, decodedBounds, scores, 1.0];
}), detectedOutputs = _a[0], boxes = _a[1], scores = _a[2], ratio = _a[3];
savedConsoleWarnFn = console.warn;
console.warn = function () { };
boxIndicesTensor = tf.image.nonMaxSuppression(boxes, scores, this.maxFaces, this.iouThreshold, this.scoreThreshold);
console.warn = savedConsoleWarnFn;
return [4 /*yield*/, boxIndicesTensor.array()];
case 1:
boxIndices = _b.sent();
boxIndicesTensor.dispose();
boundingBoxes = boxIndices.map(function (boxIndex) { return tf.slice(boxes, [boxIndex, 0], [1, -1]); });
if (!!returnTensors) return [3 /*break*/, 3];
return [4 /*yield*/, Promise.all(boundingBoxes.map(function (boundingBox) { return __awaiter(_this, void 0, void 0, function () {
var vals;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, boundingBox.array()];
case 1:
vals = _a.sent();
boundingBox.dispose();
return [2 /*return*/, vals];
}
});
}); }))];
case 2:
boundingBoxes = _b.sent();
_b.label = 3;
case 3:
if (returnTensors) {
scaleFactor = tf.div([1, 1], ratio);
}
else {
scaleFactor = [
// originalWidth / this.inputSizeData[0],
// originalHeight / this.inputSizeData[1]
1 / ratio, 1 / ratio
];
}
annotatedBoxes = [];
_loop_1 = function (i) {
var boundingBox = boundingBoxes[i];
var annotatedBox = tf.tidy(function () {
var box = boundingBox instanceof tf.Tensor ?
createBox(boundingBox) :
createBox(tf.tensor2d(boundingBox));
if (!annotateBoxes) {
return box;
}
var boxIndex = boxIndices[i];
var anchor;
if (returnTensors) {
anchor = tf.slice(_this.anchors, [boxIndex, 0], [1, 2]);
}
else {
anchor = _this.anchorsData[boxIndex];
}
var landmarks = tf.reshape(tf.squeeze(tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1])), [NUM_LANDMARKS, -1]);
var probability = tf.slice(scores, [boxIndex], [1]);
return { box: box, landmarks: landmarks, probability: probability, anchor: anchor };
});
annotatedBoxes.push(annotatedBox);
};
for (i = 0; i < boundingBoxes.length; i++) {
_loop_1(i);
}
boxes.dispose();
scores.dispose();
detectedOutputs.dispose();
return [2 /*return*/, {
boxes: annotatedBoxes,
scaleFactor: scaleFactor
}];
}
});
});
};
/**
* Returns an array of faces in an image.
*
* @param input The image to classify. Can be a tensor, DOM element image,
* video, or canvas.
* @param returnTensors (defaults to `false`) Whether to return tensors as
* opposed to values.
* @param flipHorizontal Whether to flip/mirror the facial keypoints
* horizontally. Should be true for videos that are flipped by default (e.g.
* webcams).
* @param annotateBoxes (defaults to `true`) Whether to annotate bounding
* boxes with additional properties such as landmarks and probability. Pass in
* `false` for faster inference if annotations are not needed.
*
* @return An array of detected faces, each with the following properties:
* `topLeft`: the upper left coordinate of the face in the form `[x, y]`
* `bottomRight`: the lower right coordinate of the face in the form `[x, y]`
* `landmarks`: facial landmark coordinates
* `probability`: the probability of the face being present
*/
BlazeFaceModel.prototype.estimateFaces = function (input, returnTensors, flipHorizontal, annotateBoxes) {
if (returnTensors === void 0) { returnTensors = false; }
if (flipHorizontal === void 0) { flipHorizontal = false; }
if (annotateBoxes === void 0) { annotateBoxes = true; }
return __awaiter(this, void 0, void 0, function () {
var _a, width, image, _b, boxes, scaleFactor;
var _this = this;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
_a = getInputTensorDimensions(input), width = _a[1];
image = tf.tidy(function () {
if (!(input instanceof tf.Tensor)) {
input = tf.browser.fromPixels(input);
}
return tf.expandDims(tf.cast(input, 'float32'), 0);
});
return [4 /*yield*/, this.getBoundingBoxes(image, returnTensors, annotateBoxes)];
case 1:
_b = _c.sent(), boxes = _b.boxes, scaleFactor = _b.scaleFactor;
image.dispose();
if (returnTensors) {
return [2 /*return*/, boxes.map(function (face) {
var scaledBox = scaleBoxFromPrediction(face, scaleFactor);
var normalizedFace = {
topLeft: tf.slice(scaledBox, [0], [2]),
bottomRight: tf.slice(scaledBox, [2], [2])
};
if (annotateBoxes) {
var _a = face, landmarks = _a.landmarks, probability = _a.probability, anchor = _a.anchor;
var normalizedLandmarks = tf.mul(tf.add(landmarks, anchor), scaleFactor);
normalizedFace.landmarks = normalizedLandmarks;
normalizedFace.probability = probability;
}
if (flipHorizontal) {
normalizedFace = flipFaceHorizontal(normalizedFace, width);
}
return normalizedFace;
})];
}
return [2 /*return*/, Promise.all(boxes.map(function (face) { return __awaiter(_this, void 0, void 0, function () {
var scaledBox, normalizedFace, boxData, _a, landmarkData, boxData, probabilityData, anchor_1, _b, scaleFactorX_1, scaleFactorY_1, scaledLandmarks;
var _this = this;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
scaledBox = scaleBoxFromPrediction(face, scaleFactor);
if (!!annotateBoxes) return [3 /*break*/, 2];
return [4 /*yield*/, scaledBox.array()];
case 1:
boxData = _c.sent();
normalizedFace = {
topLeft: boxData.slice(0, 2),
bottomRight: boxData.slice(2)
};
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, Promise.all([face.landmarks, scaledBox, face.probability].map(function (d) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
return [2 /*return*/, d.array()];
}); }); }))];
case 3:
_a = _c.sent(), landmarkData = _a[0], boxData = _a[1], probabilityData = _a[2];
anchor_1 = face.anchor;
_b = scaleFactor, scaleFactorX_1 = _b[0], scaleFactorY_1 = _b[1];
scaledLandmarks = landmarkData
.map(function (landmark) { return ([
(landmark[0] + anchor_1[0]) * scaleFactorX_1,
(landmark[1] + anchor_1[1]) * scaleFactorY_1
]); });
normalizedFace = {
topLeft: boxData.slice(0, 2),
bottomRight: boxData.slice(2),
landmarks: scaledLandmarks,
probability: probabilityData
};
disposeBox(face.box);
face.landmarks.dispose();
face.probability.dispose();
_c.label = 4;
case 4:
scaledBox.dispose();
if (flipHorizontal) {
normalizedFace = flipFaceHorizontal(normalizedFace, width);
}
return [2 /*return*/, normalizedFace];
}
});
}); }))];
}
});
});
};
/**
* Dispose the WebGL memory held by the underlying model.
*/
BlazeFaceModel.prototype.dispose = function () {
if (this.blazeFaceModel != null) {
this.blazeFaceModel.dispose();
}
};
return BlazeFaceModel;
}());
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var BLAZEFACE_MODEL_URL = 'https://tfhub.dev/tensorflow/tfjs-model/blazeface/1/default/1';
/**
* Load blazeface.
*
* @param config A configuration object with the following properties:
* `maxFaces` The maximum number of faces returned by the model.
* `inputWidth` The width of the input image.
* `inputHeight` The height of the input image.
* `iouThreshold` The threshold for deciding whether boxes overlap too
* much.
* `scoreThreshold` The threshold for deciding when to remove boxes based
* on score.
*/
function load(_a) {
var _b = _a === void 0 ? {} : _a, _c = _b.maxFaces, maxFaces = _c === void 0 ? 10 : _c, _d = _b.inputWidth, inputWidth = _d === void 0 ? 128 : _d, _e = _b.inputHeight, inputHeight = _e === void 0 ? 128 : _e, _f = _b.iouThreshold, iouThreshold = _f === void 0 ? 0.3 : _f, _g = _b.scoreThreshold, scoreThreshold = _g === void 0 ? 0.75 : _g, modelUrl = _b.modelUrl;
return __awaiter(this, void 0, void 0, function () {
var blazeface, model;
return __generator(this, function (_h) {
switch (_h.label) {
case 0:
if (!(modelUrl != null)) return [3 /*break*/, 2];
return [4 /*yield*/, tfconv.loadGraphModel(modelUrl)];
case 1:
blazeface = _h.sent();
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, tfconv.loadGraphModel(BLAZEFACE_MODEL_URL, {
fromTFHub: true,
})];
case 3:
blazeface = _h.sent();
_h.label = 4;
case 4:
model = new BlazeFaceModel(blazeface, inputWidth, inputHeight, maxFaces, iouThreshold, scoreThreshold);
return [2 /*return*/, model];
}
});
});
}
exports.load = load;
exports.BlazeFaceModel = BlazeFaceModel;
Object.defineProperty(exports, '__esModule', { value: true });
})));

17
thirdparty/blazeface/dist/blazeface.min.js vendored
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17
thirdparty/blazeface/dist/blazeface_test.d.ts vendored
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@ -1,17 +0,0 @@
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
export {};

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"use strict";
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
// tslint:disable-next-line: no-imports-from-dist
var jasmine_util_1 = require("@tensorflow/tfjs-core/dist/jasmine_util");
var blazeface = require("./index");
var test_util_1 = require("./test_util");
jasmine_util_1.describeWithFlags('BlazeFace', jasmine_util_1.NODE_ENVS, function () {
var model;
beforeAll(function () { return __awaiter(_this, void 0, void 0, function () {
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, blazeface.load()];
case 1:
model = _a.sent();
return [2 /*return*/];
}
});
}); });
it('estimateFaces does not leak memory', function () { return __awaiter(_this, void 0, void 0, function () {
var input, beforeTensors;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
input = tf.zeros([128, 128, 3]);
beforeTensors = tf.memory().numTensors;
return [4 /*yield*/, model.estimateFaces(input)];
case 1:
_a.sent();
expect(tf.memory().numTensors).toEqual(beforeTensors);
return [2 /*return*/];
}
});
}); });
it('estimateFaces returns objects with expected properties', function () { return __awaiter(_this, void 0, void 0, function () {
var input, result, face;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
input = tf.tensor3d(test_util_1.stubbedImageVals, [128, 128, 3]);
return [4 /*yield*/, model.estimateFaces(input)];
case 1:
result = _a.sent();
face = result[0];
expect(face.topLeft).toBeDefined();
expect(face.bottomRight).toBeDefined();
expect(face.landmarks).toBeDefined();
expect(face.probability).toBeDefined();
return [2 /*return*/];
}
});
}); });
});
//# sourceMappingURL=blazeface_test.js.map

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/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as tf from '@tensorflow/tfjs-core';
export declare type Box = {
startEndTensor: tf.Tensor2D;
startPoint: tf.Tensor2D;
endPoint: tf.Tensor2D;
};
export declare const disposeBox: (box: Box) => void;
export declare const createBox: (startEndTensor: tf.Tensor<tf.Rank.R2>) => Box;
export declare const scaleBox: (box: Box, factors: tf.Tensor<tf.Rank.R1> | [number, number]) => Box;

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"use strict";
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
exports.disposeBox = function (box) {
box.startEndTensor.dispose();
box.startPoint.dispose();
box.endPoint.dispose();
};
exports.createBox = function (startEndTensor) { return ({
startEndTensor: startEndTensor,
startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),
endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2])
}); };
exports.scaleBox = function (box, factors) {
var starts = tf.mul(box.startPoint, factors);
var ends = tf.mul(box.endPoint, factors);
var newCoordinates = tf.concat2d([starts, ends], 1);
return exports.createBox(newCoordinates);
};
//# sourceMappingURL=box.js.map

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/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as tfconv from '@tensorflow/tfjs-converter';
import * as tf from '@tensorflow/tfjs-core';
import { Box } from './box';
export interface NormalizedFace {
/** The upper left-hand corner of the face. */
topLeft: [number, number] | tf.Tensor1D;
/** The lower right-hand corner of the face. */
bottomRight: [number, number] | tf.Tensor1D;
/** Facial landmark coordinates. */
landmarks?: number[][] | tf.Tensor2D;
/** Probability of the face detection. */
probability?: number | tf.Tensor1D;
}
export declare type BlazeFacePrediction = {
box: Box;
landmarks: tf.Tensor2D;
probability: tf.Tensor1D;
anchor: tf.Tensor2D | [number, number];
};
export declare class BlazeFaceModel {
private blazeFaceModel;
private width;
private height;
private maxFaces;
private anchors;
private anchorsData;
private inputSize;
private iouThreshold;
private scoreThreshold;
constructor(model: tfconv.GraphModel, width: number, height: number, maxFaces: number, iouThreshold: number, scoreThreshold: number);
resizeAspectRatio(inputImage: tf.Tensor4D, width: number, height: number): {
ratio: number;
image: tf.Tensor<tf.Rank.R4>;
};
getBoundingBoxes(inputImage: tf.Tensor4D, returnTensors: boolean, annotateBoxes?: boolean): Promise<{
boxes: Array<BlazeFacePrediction | Box>;
scaleFactor: tf.Tensor | [number, number];
}>;
/**
* Returns an array of faces in an image.
*
* @param input The image to classify. Can be a tensor, DOM element image,
* video, or canvas.
* @param returnTensors (defaults to `false`) Whether to return tensors as
* opposed to values.
* @param flipHorizontal Whether to flip/mirror the facial keypoints
* horizontally. Should be true for videos that are flipped by default (e.g.
* webcams).
* @param annotateBoxes (defaults to `true`) Whether to annotate bounding
* boxes with additional properties such as landmarks and probability. Pass in
* `false` for faster inference if annotations are not needed.
*
* @return An array of detected faces, each with the following properties:
* `topLeft`: the upper left coordinate of the face in the form `[x, y]`
* `bottomRight`: the lower right coordinate of the face in the form `[x, y]`
* `landmarks`: facial landmark coordinates
* `probability`: the probability of the face being present
*/
estimateFaces(input: tf.Tensor3D | ImageData | HTMLVideoElement | HTMLImageElement | HTMLCanvasElement, returnTensors?: boolean, flipHorizontal?: boolean, annotateBoxes?: boolean): Promise<NormalizedFace[]>;
/**
* Dispose the WebGL memory held by the underlying model.
*/
dispose(): void;
}

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"use strict";
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var box_1 = require("./box");
// const ANCHORS_CONFIG: AnchorsConfig = {
// 'strides': [8, 16],
// 'anchors': [2, 6]
// };
// `NUM_LANDMARKS` is a fixed property of the model.
var NUM_LANDMARKS = 6;
function generateAnchors(width, height, outputSpec) {
var anchors = [];
for (var i = 0; i < outputSpec.strides.length; i++) {
var stride = outputSpec.strides[i];
var gridRows = Math.floor((height + stride - 1) / stride);
var gridCols = Math.floor((width + stride - 1) / stride);
var anchorsNum = outputSpec.anchors[i];
for (var gridY = 0; gridY < gridRows; gridY++) {
var anchorY = stride * (gridY + 0.5);
for (var gridX = 0; gridX < gridCols; gridX++) {
var anchorX = stride * (gridX + 0.5);
for (var n = 0; n < anchorsNum; n++) {
anchors.push([anchorX, anchorY]);
}
}
}
}
return anchors;
}
function decodeBounds(boxOutputs, anchors, inputSize) {
var boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);
var centers = tf.add(boxStarts, anchors);
var boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);
var boxSizesNormalized = tf.div(boxSizes, inputSize);
var centersNormalized = tf.div(centers, inputSize);
var halfBoxSize = tf.div(boxSizesNormalized, 2);
var starts = tf.sub(centersNormalized, halfBoxSize);
var ends = tf.add(centersNormalized, halfBoxSize);
var startNormalized = tf.mul(starts, inputSize);
var endNormalized = tf.mul(ends, inputSize);
var concatAxis = 1;
return tf.concat2d([startNormalized, endNormalized], concatAxis);
}
function getInputTensorDimensions(input) {
return input instanceof tf.Tensor ? [input.shape[0], input.shape[1]] :
[input.height, input.width];
}
function flipFaceHorizontal(face, imageWidth) {
var flippedTopLeft, flippedBottomRight, flippedLandmarks;
if (face.topLeft instanceof tf.Tensor &&
face.bottomRight instanceof tf.Tensor) {
var _a = tf.tidy(function () {
return [
tf.concat([
tf.slice(tf.sub(imageWidth - 1, face.topLeft), 0, 1),
tf.slice(face.topLeft, 1, 1)
]),
tf.concat([
tf.sub(imageWidth - 1, tf.slice(face.bottomRight, 0, 1)),
tf.slice(face.bottomRight, 1, 1)
])
];
}), topLeft = _a[0], bottomRight = _a[1];
flippedTopLeft = topLeft;
flippedBottomRight = bottomRight;
if (face.landmarks != null) {
flippedLandmarks = tf.tidy(function () {
var a = tf.sub(tf.tensor1d([imageWidth - 1, 0]), face.landmarks);
var b = tf.tensor1d([1, -1]);
var product = tf.mul(a, b);
return product;
});
}
}
else {
var _b = face.topLeft, topLeftX = _b[0], topLeftY = _b[1];
var _c = face.bottomRight, bottomRightX = _c[0], bottomRightY = _c[1];
flippedTopLeft = [imageWidth - 1 - topLeftX, topLeftY];
flippedBottomRight = [imageWidth - 1 - bottomRightX, bottomRightY];
if (face.landmarks != null) {
flippedLandmarks =
face.landmarks.map(function (coord) { return ([
imageWidth - 1 - coord[0],
coord[1]
]); });
}
}
var flippedFace = {
topLeft: flippedTopLeft,
bottomRight: flippedBottomRight
};
if (flippedLandmarks != null) {
flippedFace.landmarks = flippedLandmarks;
}
if (face.probability != null) {
flippedFace.probability = face.probability instanceof tf.Tensor ?
face.probability.clone() :
face.probability;
}
return flippedFace;
}
function scaleBoxFromPrediction(face, scaleFactor) {
return tf.tidy(function () {
var box;
if (face.hasOwnProperty('box')) {
box = face.box;
}
else {
box = face;
}
return tf.squeeze(box_1.scaleBox(box, scaleFactor).startEndTensor);
});
}
var BlazeFaceModel = /** @class */ (function () {
function BlazeFaceModel(model, width, height, maxFaces, iouThreshold, scoreThreshold) {
this.blazeFaceModel = model;
this.width = width;
this.height = height;
this.maxFaces = maxFaces;
var outputSpec = { strides: [width / 16, width / 8], anchors: [2, 6] };
this.anchorsData = generateAnchors(width, height, outputSpec);
this.anchors = tf.tensor2d(this.anchorsData);
// this.inputSizeData = [width, height];
this.inputSize = tf.tensor1d([width, height]);
this.iouThreshold = iouThreshold;
this.scoreThreshold = scoreThreshold;
}
BlazeFaceModel.prototype.resizeAspectRatio = function (inputImage, width, height) {
var imgWidth = inputImage.shape[2];
var imgHeight = inputImage.shape[1];
// console.log("img dim", imgWidth, imgHeight, width, height)
if (!width || !height) {
return {
ratio: 1,
image: inputImage
};
}
var ratio = Math.min(width / imgWidth, height / imgHeight);
var dimWidth = Math.round(imgWidth * ratio);
var dimHeight = Math.round(imgHeight * ratio);
// console.log("resizing to:", dimWidth, dimHeight, ratio);
return {
ratio: ratio,
image: tf.image.resizeBilinear(inputImage, [dimHeight, dimWidth])
};
};
BlazeFaceModel.prototype.getBoundingBoxes = function (inputImage, returnTensors, annotateBoxes) {
if (annotateBoxes === void 0) { annotateBoxes = true; }
return __awaiter(this, void 0, void 0, function () {
var _a, detectedOutputs, boxes, scores, ratio, savedConsoleWarnFn, boxIndicesTensor, boxIndices, boundingBoxes, scaleFactor, annotatedBoxes, _loop_1, i;
var _this = this;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = tf.tidy(function () {
var resizedImage = tf.image.resizeBilinear(inputImage, [_this.width, _this.height]);
// const resized = this.resizeAspectRatio(inputImage, this.width, this.height);
// const transform = tf.tensor2d([[1,0,0,0,1,0,0,0]]);
// const paddedImage = tf.image.transform(resized.image, transform, 'nearest', 'constant', 0, [this.width, this.height]);
var normalizedImage = tf.mul(tf.sub(tf.div(resizedImage, 255), 0.5), 2);
// [1, 897, 17] 1 = batch, 897 = number of anchors
var batchedPrediction = _this.blazeFaceModel.predict(normalizedImage);
// console.log(batchedPrediction);
var batchPred = batchedPrediction;
var sorted = batchPred.sort(function (a, b) { return a.size - b.size; });
// console.log("sorted: ", sorted);
var concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16
// console.log(concat384);
var concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16
// console.log(concat512);
var concat = tf.concat([concat512, concat384], 1);
// console.log(concat);
var prediction = tf.squeeze(concat);
// console.log(prediction);
// const prediction = tf.squeeze((batchedPrediction as tf.Tensor3D));
var decodedBounds = decodeBounds(prediction, _this.anchors, _this.inputSize);
var logits = tf.slice(prediction, [0, 0], [-1, 1]);
var scores = tf.squeeze(tf.sigmoid(logits));
return [prediction, decodedBounds, scores, 1.0];
}), detectedOutputs = _a[0], boxes = _a[1], scores = _a[2], ratio = _a[3];
savedConsoleWarnFn = console.warn;
console.warn = function () { };
boxIndicesTensor = tf.image.nonMaxSuppression(boxes, scores, this.maxFaces, this.iouThreshold, this.scoreThreshold);
console.warn = savedConsoleWarnFn;
return [4 /*yield*/, boxIndicesTensor.array()];
case 1:
boxIndices = _b.sent();
boxIndicesTensor.dispose();
boundingBoxes = boxIndices.map(function (boxIndex) { return tf.slice(boxes, [boxIndex, 0], [1, -1]); });
if (!!returnTensors) return [3 /*break*/, 3];
return [4 /*yield*/, Promise.all(boundingBoxes.map(function (boundingBox) { return __awaiter(_this, void 0, void 0, function () {
var vals;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, boundingBox.array()];
case 1:
vals = _a.sent();
boundingBox.dispose();
return [2 /*return*/, vals];
}
});
}); }))];
case 2:
boundingBoxes = _b.sent();
_b.label = 3;
case 3:
if (returnTensors) {
scaleFactor = tf.div([1, 1], ratio);
}
else {
scaleFactor = [
// originalWidth / this.inputSizeData[0],
// originalHeight / this.inputSizeData[1]
1 / ratio, 1 / ratio
];
}
annotatedBoxes = [];
_loop_1 = function (i) {
var boundingBox = boundingBoxes[i];
var annotatedBox = tf.tidy(function () {
var box = boundingBox instanceof tf.Tensor ?
box_1.createBox(boundingBox) :
box_1.createBox(tf.tensor2d(boundingBox));
if (!annotateBoxes) {
return box;
}
var boxIndex = boxIndices[i];
var anchor;
if (returnTensors) {
anchor = tf.slice(_this.anchors, [boxIndex, 0], [1, 2]);
}
else {
anchor = _this.anchorsData[boxIndex];
}
var landmarks = tf.reshape(tf.squeeze(tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1])), [NUM_LANDMARKS, -1]);
var probability = tf.slice(scores, [boxIndex], [1]);
return { box: box, landmarks: landmarks, probability: probability, anchor: anchor };
});
annotatedBoxes.push(annotatedBox);
};
for (i = 0; i < boundingBoxes.length; i++) {
_loop_1(i);
}
boxes.dispose();
scores.dispose();
detectedOutputs.dispose();
return [2 /*return*/, {
boxes: annotatedBoxes,
scaleFactor: scaleFactor
}];
}
});
});
};
/**
* Returns an array of faces in an image.
*
* @param input The image to classify. Can be a tensor, DOM element image,
* video, or canvas.
* @param returnTensors (defaults to `false`) Whether to return tensors as
* opposed to values.
* @param flipHorizontal Whether to flip/mirror the facial keypoints
* horizontally. Should be true for videos that are flipped by default (e.g.
* webcams).
* @param annotateBoxes (defaults to `true`) Whether to annotate bounding
* boxes with additional properties such as landmarks and probability. Pass in
* `false` for faster inference if annotations are not needed.
*
* @return An array of detected faces, each with the following properties:
* `topLeft`: the upper left coordinate of the face in the form `[x, y]`
* `bottomRight`: the lower right coordinate of the face in the form `[x, y]`
* `landmarks`: facial landmark coordinates
* `probability`: the probability of the face being present
*/
BlazeFaceModel.prototype.estimateFaces = function (input, returnTensors, flipHorizontal, annotateBoxes) {
if (returnTensors === void 0) { returnTensors = false; }
if (flipHorizontal === void 0) { flipHorizontal = false; }
if (annotateBoxes === void 0) { annotateBoxes = true; }
return __awaiter(this, void 0, void 0, function () {
var _a, width, image, _b, boxes, scaleFactor;
var _this = this;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
_a = getInputTensorDimensions(input), width = _a[1];
image = tf.tidy(function () {
if (!(input instanceof tf.Tensor)) {
input = tf.browser.fromPixels(input);
}
return tf.expandDims(tf.cast(input, 'float32'), 0);
});
return [4 /*yield*/, this.getBoundingBoxes(image, returnTensors, annotateBoxes)];
case 1:
_b = _c.sent(), boxes = _b.boxes, scaleFactor = _b.scaleFactor;
image.dispose();
if (returnTensors) {
return [2 /*return*/, boxes.map(function (face) {
var scaledBox = scaleBoxFromPrediction(face, scaleFactor);
var normalizedFace = {
topLeft: tf.slice(scaledBox, [0], [2]),
bottomRight: tf.slice(scaledBox, [2], [2])
};
if (annotateBoxes) {
var _a = face, landmarks = _a.landmarks, probability = _a.probability, anchor = _a.anchor;
var normalizedLandmarks = tf.mul(tf.add(landmarks, anchor), scaleFactor);
normalizedFace.landmarks = normalizedLandmarks;
normalizedFace.probability = probability;
}
if (flipHorizontal) {
normalizedFace = flipFaceHorizontal(normalizedFace, width);
}
return normalizedFace;
})];
}
return [2 /*return*/, Promise.all(boxes.map(function (face) { return __awaiter(_this, void 0, void 0, function () {
var scaledBox, normalizedFace, boxData, _a, landmarkData, boxData, probabilityData, anchor_1, _b, scaleFactorX_1, scaleFactorY_1, scaledLandmarks;
var _this = this;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
scaledBox = scaleBoxFromPrediction(face, scaleFactor);
if (!!annotateBoxes) return [3 /*break*/, 2];
return [4 /*yield*/, scaledBox.array()];
case 1:
boxData = _c.sent();
normalizedFace = {
topLeft: boxData.slice(0, 2),
bottomRight: boxData.slice(2)
};
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, Promise.all([face.landmarks, scaledBox, face.probability].map(function (d) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
return [2 /*return*/, d.array()];
}); }); }))];
case 3:
_a = _c.sent(), landmarkData = _a[0], boxData = _a[1], probabilityData = _a[2];
anchor_1 = face.anchor;
_b = scaleFactor, scaleFactorX_1 = _b[0], scaleFactorY_1 = _b[1];
scaledLandmarks = landmarkData
.map(function (landmark) { return ([
(landmark[0] + anchor_1[0]) * scaleFactorX_1,
(landmark[1] + anchor_1[1]) * scaleFactorY_1
]); });
normalizedFace = {
topLeft: boxData.slice(0, 2),
bottomRight: boxData.slice(2),
landmarks: scaledLandmarks,
probability: probabilityData
};
box_1.disposeBox(face.box);
face.landmarks.dispose();
face.probability.dispose();
_c.label = 4;
case 4:
scaledBox.dispose();
if (flipHorizontal) {
normalizedFace = flipFaceHorizontal(normalizedFace, width);
}
return [2 /*return*/, normalizedFace];
}
});
}); }))];
}
});
});
};
/**
* Dispose the WebGL memory held by the underlying model.
*/
BlazeFaceModel.prototype.dispose = function () {
if (this.blazeFaceModel != null) {
this.blazeFaceModel.dispose();
}
};
return BlazeFaceModel;
}());
exports.BlazeFaceModel = BlazeFaceModel;
//# sourceMappingURL=face.js.map

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/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as tf from '@tensorflow/tfjs-core';
import { BlazeFaceModel } from './face';
interface BlazeFaceConfig {
maxFaces?: number;
inputWidth?: number;
inputHeight?: number;
iouThreshold?: number;
scoreThreshold?: number;
modelUrl?: string | tf.io.IOHandler;
}
/**
* Load blazeface.
*
* @param config A configuration object with the following properties:
* `maxFaces` The maximum number of faces returned by the model.
* `inputWidth` The width of the input image.
* `inputHeight` The height of the input image.
* `iouThreshold` The threshold for deciding whether boxes overlap too
* much.
* `scoreThreshold` The threshold for deciding when to remove boxes based
* on score.
*/
export declare function load({ maxFaces, inputWidth, inputHeight, iouThreshold, scoreThreshold, modelUrl, }?: BlazeFaceConfig): Promise<BlazeFaceModel>;
export { NormalizedFace, BlazeFaceModel, BlazeFacePrediction } from './face';

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"use strict";
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
var tfconv = require("@tensorflow/tfjs-converter");
var face_1 = require("./face");
var BLAZEFACE_MODEL_URL = 'https://tfhub.dev/tensorflow/tfjs-model/blazeface/1/default/1';
/**
* Load blazeface.
*
* @param config A configuration object with the following properties:
* `maxFaces` The maximum number of faces returned by the model.
* `inputWidth` The width of the input image.
* `inputHeight` The height of the input image.
* `iouThreshold` The threshold for deciding whether boxes overlap too
* much.
* `scoreThreshold` The threshold for deciding when to remove boxes based
* on score.
*/
function load(_a) {
var _b = _a === void 0 ? {} : _a, _c = _b.maxFaces, maxFaces = _c === void 0 ? 10 : _c, _d = _b.inputWidth, inputWidth = _d === void 0 ? 128 : _d, _e = _b.inputHeight, inputHeight = _e === void 0 ? 128 : _e, _f = _b.iouThreshold, iouThreshold = _f === void 0 ? 0.3 : _f, _g = _b.scoreThreshold, scoreThreshold = _g === void 0 ? 0.75 : _g, modelUrl = _b.modelUrl;
return __awaiter(this, void 0, void 0, function () {
var blazeface, model;
return __generator(this, function (_h) {
switch (_h.label) {
case 0:
if (!(modelUrl != null)) return [3 /*break*/, 2];
return [4 /*yield*/, tfconv.loadGraphModel(modelUrl)];
case 1:
blazeface = _h.sent();
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, tfconv.loadGraphModel(BLAZEFACE_MODEL_URL, {
fromTFHub: true,
})];
case 3:
blazeface = _h.sent();
_h.label = 4;
case 4:
model = new face_1.BlazeFaceModel(blazeface, inputWidth, inputHeight, maxFaces, iouThreshold, scoreThreshold);
return [2 /*return*/, model];
}
});
});
}
exports.load = load;
var face_2 = require("./face");
exports.BlazeFaceModel = face_2.BlazeFaceModel;
//# sourceMappingURL=index.js.map

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/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
export declare const stubbedImageVals: number[];

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/** @license See the LICENSE file. */
declare const version = "0.0.7";
export { version };

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"use strict";
/** @license See the LICENSE file. */
Object.defineProperty(exports, "__esModule", { value: true });
// This code is auto-generated, do not modify this file!
var version = '0.0.7';
exports.version = version;
//# sourceMappingURL=version.js.map

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{"version":3,"file":"version.js","sourceRoot":"","sources":["../src/version.ts"],"names":[],"mappings":";AAAA,qCAAqC;;AAErC,wDAAwD;AACxD,IAAM,OAAO,GAAG,OAAO,CAAC;AAChB,0BAAO"}

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{
"name": "@tensorflow-models/blazeface",
"version": "0.0.7-ente",
"description": "Pretrained face detection model in TensorFlow.js",
"main": "dist/index.js",
"jsnext:main": "dist/blazeface.esm.js",
"module": "dist/blazeface.esm.js",
"unpkg": "dist/blazeface.min.js",
"jsdelivr": "dist/blazeface.min.js",
"types": "dist/index.d.ts",
"repository": {
"type": "git",
"url": "https://github.com/tensorflow/tfjs-models.git"
},
"peerDependencies": {
"@tensorflow/tfjs-converter": "^3.1.0",
"@tensorflow/tfjs-core": "^3.1.0"
},
"devDependencies": {
"@tensorflow/tfjs-backend-cpu": "^3.1.0",
"@tensorflow/tfjs-converter": "^3.1.0",
"@tensorflow/tfjs-core": "^3.1.0",
"@types/jasmine": "~2.5.53",
"jasmine": "^3.7.0",
"jasmine-core": "^3.7.1",
"rimraf": "~2.6.2",
"rollup": "~0.58.2",
"rollup-plugin-node-resolve": "~3.3.0",
"rollup-plugin-typescript2": "~0.13.0",
"rollup-plugin-uglify": "~3.0.0",
"ts-node": "~5.0.0",
"tslint": "~6.1.3",
"typescript": "3.5.3",
"yalc": "^1.0.0-pre.50"
},
"scripts": {
"build": "rimraf dist && tsc",
"publish-local": "yarn build && rollup -c && yalc publish",
"test": "ts-node --skip-ignore --project tsconfig.test.json run_tests.ts",
"build-npm": "yarn build && rollup -c",
"lint": "tslint -p . -t verbose",
"publish-demo": "./scripts/publish-demo.sh"
},
"license": "Apache-2.0"
}

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@ -1399,9 +1399,6 @@
ejs "^2.6.1"
magic-string "^0.25.0"
"@tensorflow-models/blazeface@./thirdparty/blazeface":
version "0.0.7-ente"
"@tensorflow/tfjs-backend-cpu@3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-3.11.0.tgz#01d5d68b91faf12bee4854adae56bc956b794f1a"
@ -2260,6 +2257,11 @@ bip39@^3.0.4:
pbkdf2 "^3.0.9"
randombytes "^2.0.1"
blazeface-back@^0.0.8:
version "0.0.8"
resolved "https://registry.yarnpkg.com/blazeface-back/-/blazeface-back-0.0.8.tgz#d997a9cfcd5b7ad03367d65d7c0b365c5a835b83"
integrity sha512-7eYoSPJ9JvV2cHp+qwW+YNvrdTDFzMGnSGCGR24Lx6OluwPa+qX4NqNrBv31ukw2Ne/FkvFHhZuUDpxpowr+Bg==
bluebird@^3.5.5:
version "3.7.2"
resolved "https://registry.npmjs.org/bluebird/-/bluebird-3.7.2.tgz"