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Unverified Commit f697eec1 by justadudewhohacks Committed by GitHub
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Merge pull request #62 from justadudewhohacks/tiny-yolov2

Added an implementation of Tiny Yolo v2 for face detection
parents 76e80680 cd43f793
Showing with 1896 additions and 75 deletions
.gitignore
node_modules
.rpt2_cache
.env*
tmp
\ No newline at end of file
tmp
weights_uncompressed
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README.md
......@@ -9,17 +9,21 @@ Check out my face-api.js tutorials:
* **[face-api.js — JavaScript API for Face Recognition in the Browser with tensorflow.js](https://itnext.io/face-api-js-javascript-api-for-face-recognition-in-the-browser-with-tensorflow-js-bcc2a6c4cf07)**
* **[Realtime JavaScript Face Tracking and Face Recognition using face-api.js’ MTCNN Face Detector](https://itnext.io/realtime-javascript-face-tracking-and-face-recognition-using-face-api-js-mtcnn-face-detector-d924dd8b5740)**
**Check out the live demos [here](https://justadudewhohacks.github.io/face-api.js/)!**
Table of Contents:
* **[Running the Examples](#running-the-examples)**
* **[About the Package](#about-the-package)**
* **[Face Detection - SSD Mobilenet v1](#about-face-detection-ssd)**
* **[Face Detection - Tiny Yolo v2](#about-face-detection-yolo)**
* **[Face Detection & 5 Point Face Landmarks - MTCNN](#about-face-detection-mtcnn)**
* **[Face Recognition](#about-face-recognition)**
* **[68 Point Face Landmark Detection](#about-face-landmark-detection)**
* **[Usage](#usage)**
* **[Loading the Models](#usage-load-models)**
* **[Face Detection - SSD Mobilenet v1](#usage-face-detection-ssd)**
* **[Face Detection - Tiny Yolo v2](#usage-face-detection-yolo)**
* **[Face Detection & 5 Point Face Landmarks - MTCNN](#usage-face-detection-mtcnn)**
* **[Face Recognition](#usage-face-recognition)**
* **[68 Point Face Landmark Detection](#usage-face-landmark-detection)**
......@@ -27,8 +31,6 @@ Table of Contents:
## Examples
### **Check out the live demos [here](https://justadudewhohacks.github.io/face-api.js/)!**
### Face Recognition
![preview_face-detection-and-recognition](https://user-images.githubusercontent.com/31125521/41526995-1a90e4e6-72e6-11e8-96d4-8b2ccdee5f79.gif)
......@@ -83,11 +85,19 @@ For face detection, this project implements a SSD (Single Shot Multibox Detector
The face detection model has been trained on the [WIDERFACE dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and the weights are provided by [yeephycho](https://github.com/yeephycho) in [this](https://github.com/yeephycho/tensorflow-face-detection) repo.
<a name="about-face-detection-yolo"></a>
### Face Detection - Tiny Yolo v2
The Tiny Yolo v2 based face detector can easily adapt to different input image sizes, thus can be used as an alternative to SSD Mobilenet v1 to trade off accuracy for performance (inference time). In general the model is not as accurate as SSD Mobilenet v1 but can achieve faster inference for lower image sizes.
The Tiny Yolo v2 implementation is still experimental, meaning there is room for optimization (future work). The trained model weights are provided in the [azFace](https://github.com/azmathmoosa/azFace) project.
<a name="about-face-detection-mtcnn"></a>
### Face Detection & 5 Point Face Landmarks - MTCNN
MTCNN (Multi-task Cascaded Convolutional Neural Networks) represents an alternative to SSD Mobilenet v1, which offers much more room for configuration and is able to achieve much lower processing times. MTCNN is a 3 stage cascaded CNN, which simultanously returns 5 face landmark points along with the bounding boxes and scores for each face. By limiting the minimum size of faces expected in an image, MTCNN allows you to process frames from your webcam in realtime. Additionally with 2MB, the size of the weights file is only a third of the size of the quantized SSD Mobilenet v1 model (~6MB).
MTCNN (Multi-task Cascaded Convolutional Neural Networks) represents an alternative face detector to SSD Mobilenet v1 and Tiny Yolo v2, which offers much more room for configuration and is able to achieve much lower processing times. MTCNN is a 3 stage cascaded CNN, which simultanously returns 5 face landmark points along with the bounding boxes and scores for each face. By limiting the minimum size of faces expected in an image, MTCNN allows you to process frames from your webcam in realtime. Additionally with 2MB, the size of the weights file is only a third of the size of the quantized SSD Mobilenet v1 model (~6MB).
MTCNN has been presented in the paper [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf) by Zhang et al. and the model weights are provided in the official [repo](https://github.com/kpzhang93/MTCNN_face_detection_alignment) of the MTCNN implementation.
......@@ -137,6 +147,7 @@ await faceapi.loadFaceDetectionModel('/models')
// await faceapi.loadFaceLandmarkModel('/models')
// await faceapi.loadFaceRecognitionModel('/models')
// await faceapi.loadMtcnnModel('/models')
// await faceapi.loadTinyYolov2Model('/models')
```
As an alternative, you can also create instance of the neural nets:
......@@ -147,11 +158,13 @@ const net = new faceapi.FaceDetectionNet()
// const net = new faceapi.FaceLandmarkNet()
// const net = new faceapi.FaceRecognitionNet()
// const net = new faceapi.Mtcnn()
// const net = new faceapi.TinyYolov2()
await net.load('/models/face_detection_model-weights_manifest.json')
// await net.load('/models/face_landmark_68_model-weights_manifest.json')
// await net.load('/models/face_recognition_model-weights_manifest.json')
// await net.load('/models/mtcnn_model-weights_manifest.json')
// await net.load('/models/tiny_yolov2_model-weights_manifest.json')
// or simply load all models
await net.load('/models')
......@@ -204,6 +217,24 @@ You can also obtain the tensors of the unfiltered bounding boxes and scores for
const { boxes, scores } = await net.forward('myImg')
```
<a name="usage-face-detection-yolo"></a>
### Face Detection - Tiny Yolo v2
Detect faces and get the bounding boxes and scores:
``` javascript
// defaults parameters shown:
const forwardParams = {
scoreThreshold: 0.5,
// any number or one of the predifened sizes:
// 'xs' (224 x 224) | 'sm' (320 x 320) | 'md' (416 x 416) | 'lg' (608 x 608)
inputSize: 'md'
}
const detections = await faceapi.tinyYolov2(document.getElementById('myImg'), forwardParams)
```
<a name="usage-face-detection-mtcnn"></a>
### Face Detection & 5 Point Face Landmarks - MTCNN
......
build/BoundingBox.d.ts 0 → 100644
import { Rect } from './Rect';
export declare class BoundingBox {
private _left;
private _top;
private _right;
private _bottom;
constructor(_left: number, _top: number, _right: number, _bottom: number);
readonly left: number;
readonly top: number;
readonly right: number;
readonly bottom: number;
readonly width: number;
readonly height: number;
toSquare(): BoundingBox;
round(): BoundingBox;
padAtBorders(imageHeight: number, imageWidth: number): {
dy: number;
edy: number;
dx: number;
edx: number;
y: number;
ey: number;
x: number;
ex: number;
w: number;
h: number;
};
calibrate(region: BoundingBox): BoundingBox;
toRect(): Rect;
}
build/BoundingBox.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("./Rect");
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
this._top = _top;
this._right = _right;
this._bottom = _bottom;
}
Object.defineProperty(BoundingBox.prototype, "left", {
get: function () {
return this._left;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "top", {
get: function () {
return this._top;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "right", {
get: function () {
return this._right;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "bottom", {
get: function () {
return this._bottom;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "width", {
get: function () {
return this.right - this.left;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "height", {
get: function () {
return this.bottom - this.top;
},
enumerable: true,
configurable: true
});
BoundingBox.prototype.toSquare = function () {
var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
var off = (Math.abs(this.width - this.height) / 2);
if (this.width < this.height) {
left -= off;
right += off;
}
if (this.height < this.width) {
top -= off;
bottom += off;
}
return new BoundingBox(left, top, right, bottom);
};
BoundingBox.prototype.round = function () {
return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom));
};
BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) {
var w = this.width + 1;
var h = this.height + 1;
var dx = 1;
var dy = 1;
var edx = w;
var edy = h;
var x = this.left;
var y = this.top;
var ex = this.right;
var ey = this.bottom;
if (ex > imageWidth) {
edx = -ex + imageWidth + w;
ex = imageWidth;
}
if (ey > imageHeight) {
edy = -ey + imageHeight + h;
ey = imageHeight;
}
if (x < 1) {
edy = 2 - x;
x = 1;
}
if (y < 1) {
edy = 2 - y;
y = 1;
}
return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
};
BoundingBox.prototype.calibrate = function (region) {
return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
};
BoundingBox.prototype.toRect = function () {
return new Rect_1.Rect(this.left, this.top, this.width, this.height);
};
return BoundingBox;
}());
exports.BoundingBox = BoundingBox;
//# sourceMappingURL=BoundingBox.js.map
\ No newline at end of file
build/BoundingBox.js.map 0 → 100644
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\ No newline at end of file
build/commons/nonMaxSuppression.d.ts 0 → 100644
import { BoundingBox } from '../BoundingBox';
export declare function nonMaxSuppression(boxes: BoundingBox[], scores: number[], iouThreshold: number, isIOU?: boolean): number[];
build/commons/nonMaxSuppression.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {
if (isIOU === void 0) { isIOU = true; }
var areas = boxes.map(function (box) { return (box.width + 1) * (box.height + 1); });
var indicesSortedByScore = scores
.map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
.sort(function (c1, c2) { return c1.score - c2.score; })
.map(function (c) { return c.boxIndex; });
var pick = [];
var _loop_1 = function () {
var curr = indicesSortedByScore.pop();
pick.push(curr);
var indices = indicesSortedByScore;
var outputs = [];
for (var i = 0; i < indices.length; i++) {
var idx = indices[i];
var currBox = boxes[curr];
var idxBox = boxes[idx];
var width = Math.max(0.0, Math.min(currBox.right, idxBox.right) - Math.max(currBox.left, idxBox.left) + 1);
var height = Math.max(0.0, Math.min(currBox.bottom, idxBox.bottom) - Math.max(currBox.top, idxBox.top) + 1);
var interSection = width * height;
var out = isIOU
? interSection / (areas[curr] + areas[idx] - interSection)
: interSection / Math.min(areas[curr], areas[idx]);
outputs.push(out);
}
indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
};
while (indicesSortedByScore.length > 0) {
_loop_1();
}
return pick;
}
exports.nonMaxSuppression = nonMaxSuppression;
//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file
build/commons/nonMaxSuppression.js.map 0 → 100644
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\ No newline at end of file
build/faceDetectionNet/loadQuantizedParams.js
......@@ -5,7 +5,7 @@ var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensor
var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
var isTensor_1 = require("../commons/isTensor");
var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'face_detection_model';
var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
......
build/faceDetectionNet/loadQuantizedParams.js.map
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\ No newline at end of file
build/globalApi.d.ts
......@@ -8,6 +8,8 @@ import { FullFaceDescription } from './FullFaceDescription';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { NetInput } from './NetInput';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
import { TinyYolov2ForwardParams } from './tinyYolov2/types';
import { TNetInput } from './types';
export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmarkNet;
......@@ -17,16 +19,19 @@ export declare const nets: {
faceLandmark68Net: FaceLandmarkNet;
faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn;
tinyYolov2: TinyYolov2;
};
export declare function loadFaceDetectionModel(url: string): Promise<void>;
export declare function loadFaceLandmarkModel(url: string): Promise<void>;
export declare function loadFaceRecognitionModel(url: string): Promise<void>;
export declare function loadMtcnnModel(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void, void]>;
export declare function loadTinyYolov2Model(url: string): Promise<void>;
export declare function loadModels(url: string): Promise<[void, void, void, void, void]>;
export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
export declare type allFacesFunction = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare const allFaces: allFacesFunction;
export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
......
build/globalApi.js
......@@ -5,6 +5,7 @@ var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn");
var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
exports.landmarkNet = new FaceLandmarkNet_1.FaceLandmarkNet();
exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
......@@ -14,7 +15,8 @@ exports.nets = {
ssdMobilenet: exports.detectionNet,
faceLandmark68Net: exports.landmarkNet,
faceRecognitionNet: exports.recognitionNet,
mtcnn: new Mtcnn_1.Mtcnn()
mtcnn: new Mtcnn_1.Mtcnn(),
tinyYolov2: new TinyYolov2_1.TinyYolov2()
};
function loadFaceDetectionModel(url) {
return exports.nets.ssdMobilenet.load(url);
......@@ -32,12 +34,17 @@ function loadMtcnnModel(url) {
return exports.nets.mtcnn.load(url);
}
exports.loadMtcnnModel = loadMtcnnModel;
function loadTinyYolov2Model(url) {
return exports.nets.tinyYolov2.load(url);
}
exports.loadTinyYolov2Model = loadTinyYolov2Model;
function loadModels(url) {
return Promise.all([
loadFaceDetectionModel(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url),
loadMtcnnModel(url)
loadMtcnnModel(url),
loadTinyYolov2Model(url)
]);
}
exports.loadModels = loadModels;
......@@ -57,6 +64,10 @@ function mtcnn(input, forwardParams) {
return exports.nets.mtcnn.forward(input, forwardParams);
}
exports.mtcnn = mtcnn;
function tinyYolov2(input, forwardParams) {
return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
}
exports.tinyYolov2 = tinyYolov2;
exports.allFaces = allFacesFactory_1.allFacesFactory(exports.nets.ssdMobilenet, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
//# sourceMappingURL=globalApi.js.map
\ No newline at end of file
build/globalApi.js.map
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\ No newline at end of file
build/index.d.ts
......@@ -15,5 +15,6 @@ export * from './faceRecognitionNet';
export * from './globalApi';
export * from './mtcnn';
export * from './padToSquare';
export * from './tinyYolov2';
export * from './toNetInput';
export * from './utils';
build/index.js
......@@ -18,6 +18,7 @@ tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
tslib_1.__exportStar(require("./globalApi"), exports);
tslib_1.__exportStar(require("./mtcnn"), exports);
tslib_1.__exportStar(require("./padToSquare"), exports);
tslib_1.__exportStar(require("./tinyYolov2"), exports);
tslib_1.__exportStar(require("./toNetInput"), exports);
tslib_1.__exportStar(require("./utils"), exports);
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/index.js.map
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\ No newline at end of file
build/mtcnn/BoundingBox.d.ts
import { Rect } from '../Rect';
export declare class BoundingBox {
private _left;
private _top;
......@@ -25,4 +26,5 @@ export declare class BoundingBox {
h: number;
};
calibrate(region: BoundingBox): BoundingBox;
toRect(): Rect;
}
build/mtcnn/BoundingBox.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Rect_1 = require("../Rect");
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
......@@ -97,6 +98,9 @@ var BoundingBox = /** @class */ (function () {
BoundingBox.prototype.calibrate = function (region) {
return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
};
BoundingBox.prototype.toRect = function () {
return new Rect_1.Rect(this.left, this.top, this.width, this.height);
};
return BoundingBox;
}());
exports.BoundingBox = BoundingBox;
......
build/mtcnn/BoundingBox.js.map
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\ No newline at end of file
build/mtcnn/extractImagePatches.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from '../BoundingBox';
import { Dimensions } from '../types';
import { BoundingBox } from './BoundingBox';
export declare function extractImagePatches(img: HTMLCanvasElement, boxes: BoundingBox[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>;
build/mtcnn/extractImagePatches.js.map
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build/mtcnn/stage1.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from './BoundingBox';
import { BoundingBox } from '../BoundingBox';
import { PNetParams } from './types';
export declare function stage1(imgTensor: tf.Tensor4D, scales: number[], scoreThreshold: number, params: PNetParams, stats: any): {
boxes: BoundingBox[];
......
build/mtcnn/stage1.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var BoundingBox_1 = require("../BoundingBox");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var Point_1 = require("../Point");
var BoundingBox_1 = require("./BoundingBox");
var config_1 = require("./config");
var nms_1 = require("./nms");
var getSizesForScale_1 = require("./getSizesForScale");
var normalize_1 = require("./normalize");
var PNet_1 = require("./PNet");
var getSizesForScale_1 = require("./getSizesForScale");
function rescaleAndNormalize(x, scale) {
return tf.tidy(function () {
var _a = getSizesForScale_1.getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
......@@ -65,7 +65,7 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
return [];
}
var ts = Date.now();
var indices = nms_1.nms(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
var indices = nonMaxSuppression_1.nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
statsForScale.nms = Date.now() - ts;
statsForScale.numBoxes = indices.length;
stats.stage1.push(statsForScale);
......@@ -76,7 +76,7 @@ function stage1(imgTensor, scales, scoreThreshold, params, stats) {
var finalScores = [];
if (allBoxes.length > 0) {
var ts = Date.now();
var indices = nms_1.nms(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
var indices = nonMaxSuppression_1.nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
stats.stage1_nms = Date.now() - ts;
finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
finalBoxes = indices
......
build/mtcnn/stage1.js.map
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build/mtcnn/stage2.d.ts
import { BoundingBox } from './BoundingBox';
import { BoundingBox } from '../BoundingBox';
import { RNetParams } from './types';
export declare function stage2(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: RNetParams, stats: any): Promise<{
boxes: BoundingBox[];
......
build/mtcnn/stage2.js
......@@ -2,9 +2,9 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var BoundingBox_1 = require("./BoundingBox");
var BoundingBox_1 = require("../BoundingBox");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var extractImagePatches_1 = require("./extractImagePatches");
var nms_1 = require("./nms");
var RNet_1 = require("./RNet");
function stage2(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
......@@ -45,7 +45,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
finalScores = [];
if (filteredBoxes.length > 0) {
ts = Date.now();
indicesNms = nms_1.nms(filteredBoxes, filteredScores, 0.7);
indicesNms = nonMaxSuppression_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
stats.stage2_nms = Date.now() - ts;
regions_1 = indicesNms.map(function (idx) {
return new BoundingBox_1.BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
......
build/mtcnn/stage2.js.map
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\ No newline at end of file
build/mtcnn/stage3.d.ts
import { BoundingBox } from '../BoundingBox';
import { Point } from '../Point';
import { BoundingBox } from './BoundingBox';
import { ONetParams } from './types';
export declare function stage3(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: ONetParams, stats: any): Promise<{
boxes: BoundingBox[];
......
build/mtcnn/stage3.js
......@@ -2,10 +2,10 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var BoundingBox_1 = require("../BoundingBox");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var Point_1 = require("../Point");
var BoundingBox_1 = require("./BoundingBox");
var extractImagePatches_1 = require("./extractImagePatches");
var nms_1 = require("./nms");
var ONet_1 = require("./ONet");
function stage3(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
......@@ -49,7 +49,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
points = [];
if (filteredBoxes.length > 0) {
ts = Date.now();
indicesNms = nms_1.nms(filteredBoxes, filteredScores, 0.7, false);
indicesNms = nonMaxSuppression_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
stats.stage3_nms = Date.now() - ts;
finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
......
build/mtcnn/stage3.js.map
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\ No newline at end of file
build/tinyYolov2/TinyYolov2.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { FaceDetection } from '../FaceDetection';
import { NetInput } from '../NetInput';
import { TNetInput } from '../types';
import { NetParams, TinyYolov2ForwardParams } from './types';
export declare class TinyYolov2 extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput, inputSize: number): tf.Tensor4D;
forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D>;
locateFaces(input: TNetInput, forwardParams?: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/tinyYolov2/TinyYolov2.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var BoundingBox_1 = require("../BoundingBox");
var convLayer_1 = require("../commons/convLayer");
var NeuralNetwork_1 = require("../commons/NeuralNetwork");
var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
var FaceDetection_1 = require("../FaceDetection");
var toNetInput_1 = require("../toNetInput");
var config_1 = require("./config");
var convWithBatchNorm_1 = require("./convWithBatchNorm");
var extractParams_1 = require("./extractParams");
var getDefaultParams_1 = require("./getDefaultParams");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
var TinyYolov2 = /** @class */ (function (_super) {
tslib_1.__extends(TinyYolov2, _super);
function TinyYolov2() {
return _super.call(this, 'TinyYolov2') || this;
}
TinyYolov2.prototype.forwardInput = function (input, inputSize) {
var params = this.params;
if (!params) {
throw new Error('TinyYolov2 - load model before inference');
}
var out = tf.tidy(function () {
var batchTensor = input.toBatchTensor(inputSize, false).div(tf.scalar(255)).toFloat();
var out = convWithBatchNorm_1.convWithBatchNorm(batchTensor, params.conv0);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv1);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv2);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv3);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv4);
out = tf.maxPool(out, [2, 2], [2, 2], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv5);
out = tf.maxPool(out, [2, 2], [1, 1], 'same');
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv6);
out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv7);
out = convLayer_1.convLayer(out, params.conv8, 'valid', false);
return out;
});
return out;
};
TinyYolov2.prototype.forward = function (input, inputSize) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput_1.toNetInput(input, true, true)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
case 2: return [2 /*return*/, _b.sent()];
}
});
});
};
TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a, _inputSize, scoreThreshold, inputSize, netInput, out, numCells, _b, boxesTensor, scoresTensor, expit, paddedHeightRelative, paddedWidthRelative, boxes, scores, row, col, box, score, ctX, ctY, width, height, x, y, indices, detections;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
_a = getDefaultParams_1.getDefaultParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
inputSize = typeof _inputSize === 'string'
? config_1.INPUT_SIZES[_inputSize]
: _inputSize;
if (typeof inputSize !== 'number') {
throw new Error("TinyYolov2 - unkown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
}
return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
case 1:
netInput = _c.sent();
return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
case 2:
out = _c.sent();
numCells = out.shape[1];
_b = tf.tidy(function () {
var reshaped = out.reshape([numCells, numCells, config_1.NUM_BOXES, 6]);
out.dispose();
var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, config_1.NUM_BOXES, 4]);
var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, config_1.NUM_BOXES, 1]);
return [boxes, scores];
}), boxesTensor = _b[0], scoresTensor = _b[1];
expit = function (x) { return 1 / (1 + Math.exp(-x)); };
paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
boxes = [];
scores = [];
for (row = 0; row < numCells; row++) {
for (col = 0; col < numCells; col++) {
for (box = 0; box < config_1.NUM_BOXES; box++) {
score = expit(scoresTensor.get(row, col, box, 0));
if (score > scoreThreshold) {
ctX = ((col + expit(boxesTensor.get(row, col, box, 0))) / numCells) * paddedWidthRelative;
ctY = ((row + expit(boxesTensor.get(row, col, box, 1))) / numCells) * paddedHeightRelative;
width = ((Math.exp(boxesTensor.get(row, col, box, 2)) * config_1.BOX_ANCHORS[box].x) / numCells) * paddedWidthRelative;
height = ((Math.exp(boxesTensor.get(row, col, box, 3)) * config_1.BOX_ANCHORS[box].y) / numCells) * paddedHeightRelative;
x = (ctX - (width / 2));
y = (ctY - (height / 2));
boxes.push(new BoundingBox_1.BoundingBox(x, y, x + width, y + height));
scores.push(score);
}
}
}
}
boxesTensor.dispose();
scoresTensor.dispose();
indices = nonMaxSuppression_1.nonMaxSuppression(boxes.map(function (box) { return new BoundingBox_1.BoundingBox(box.left * inputSize, box.top * inputSize, box.right * inputSize, box.bottom * inputSize); }), scores, config_1.IOU_THRESHOLD, true);
detections = indices.map(function (idx) {
return new FaceDetection_1.FaceDetection(scores[idx], boxes[idx].toRect(), { width: netInput.getInputWidth(0), height: netInput.getInputHeight(0) });
});
return [2 /*return*/, detections];
}
});
});
};
TinyYolov2.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
TinyYolov2.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return TinyYolov2;
}(NeuralNetwork_1.NeuralNetwork));
exports.TinyYolov2 = TinyYolov2;
//# sourceMappingURL=TinyYolov2.js.map
\ No newline at end of file
build/tinyYolov2/TinyYolov2.js.map 0 → 100644
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build/tinyYolov2/config.d.ts 0 → 100644
import { Point } from '../Point';
export declare const INPUT_SIZES: {
xs: number;
sm: number;
md: number;
lg: number;
};
export declare const NUM_BOXES = 5;
export declare const IOU_THRESHOLD = 0.4;
export declare const BOX_ANCHORS: Point[];
build/tinyYolov2/config.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var Point_1 = require("../Point");
exports.INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 };
exports.NUM_BOXES = 5;
exports.IOU_THRESHOLD = 0.4;
exports.BOX_ANCHORS = [
new Point_1.Point(0.738768, 0.874946),
new Point_1.Point(2.42204, 2.65704),
new Point_1.Point(4.30971, 7.04493),
new Point_1.Point(10.246, 4.59428),
new Point_1.Point(12.6868, 11.8741)
];
//# sourceMappingURL=config.js.map
\ No newline at end of file
build/tinyYolov2/config.js.map 0 → 100644
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\ No newline at end of file
build/tinyYolov2/convWithBatchNorm.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvWithBatchNorm } from './types';
export declare function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D;
build/tinyYolov2/convWithBatchNorm.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var leaky_1 = require("./leaky");
function convWithBatchNorm(x, params) {
return tf.tidy(function () {
var out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');
out = tf.sub(out, params.bn.sub);
out = tf.mul(out, params.bn.truediv);
out = tf.add(out, params.conv.bias);
return leaky_1.leaky(out);
});
}
exports.convWithBatchNorm = convWithBatchNorm;
//# sourceMappingURL=convWithBatchNorm.js.map
\ No newline at end of file
build/tinyYolov2/convWithBatchNorm.js.map 0 → 100644
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\ No newline at end of file
build/tinyYolov2/extractParams.d.ts 0 → 100644
import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
build/tinyYolov2/extractParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
function extractorsFactory(extractWeights, paramMappings) {
var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
function extractBatchNormParams(size, mappedPrefix) {
var sub = tf.tensor1d(extractWeights(size));
var truediv = tf.tensor1d(extractWeights(size));
paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
return { sub: sub, truediv: truediv };
}
function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
return { conv: conv, bn: bn };
}
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams
};
}
function extractParams(weights) {
var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var paramMappings = [];
var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams;
var conv0 = extractConvWithBatchNormParams(3, 16, 'conv0');
var conv1 = extractConvWithBatchNormParams(16, 32, 'conv1');
var conv2 = extractConvWithBatchNormParams(32, 64, 'conv2');
var conv3 = extractConvWithBatchNormParams(64, 128, 'conv3');
var conv4 = extractConvWithBatchNormParams(128, 256, 'conv4');
var conv5 = extractConvWithBatchNormParams(256, 512, 'conv5');
var conv6 = extractConvWithBatchNormParams(512, 1024, 'conv6');
var conv7 = extractConvWithBatchNormParams(1024, 1024, 'conv7');
var conv8 = extractConvParams(1024, 30, 1, 'conv8');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
var params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
return { params: params, paramMappings: paramMappings };
}
exports.extractParams = extractParams;
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
build/tinyYolov2/extractParams.js.map 0 → 100644
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\ No newline at end of file
build/tinyYolov2/getDefaultParams.d.ts 0 → 100644
import { SizeType, TinyYolov2ForwardParams } from './types';
export declare function getDefaultParams(params: TinyYolov2ForwardParams): {
sizeType: SizeType;
scoreThreshold: number;
} & TinyYolov2ForwardParams;
build/tinyYolov2/getDefaultParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var types_1 = require("./types");
function getDefaultParams(params) {
return Object.assign({}, {
sizeType: types_1.SizeType.MD,
scoreThreshold: 0.5
}, params);
}
exports.getDefaultParams = getDefaultParams;
//# sourceMappingURL=getDefaultParams.js.map
\ No newline at end of file
build/tinyYolov2/getDefaultParams.js.map 0 → 100644
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build/tinyYolov2/index.d.ts 0 → 100644
import { TinyYolov2 } from './TinyYolov2';
export * from './TinyYolov2';
export declare function createTinyYolov2(weights: Float32Array): TinyYolov2;
build/tinyYolov2/index.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var TinyYolov2_1 = require("./TinyYolov2");
tslib_1.__exportStar(require("./TinyYolov2"), exports);
function createTinyYolov2(weights) {
var net = new TinyYolov2_1.TinyYolov2();
net.extractWeights(weights);
return net;
}
exports.createTinyYolov2 = createTinyYolov2;
//# sourceMappingURL=index.js.map
\ No newline at end of file
build/tinyYolov2/index.js.map 0 → 100644
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build/tinyYolov2/leaky.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export declare function leaky(x: tf.Tensor4D): tf.Tensor4D;
build/tinyYolov2/leaky.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs-core");
function leaky(x) {
return tf.tidy(function () {
return tf.maximum(x, tf.mul(x, tf.scalar(0.10000000149011612)));
});
}
exports.leaky = leaky;
//# sourceMappingURL=leaky.js.map
\ No newline at end of file
build/tinyYolov2/leaky.js.map 0 → 100644
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\ No newline at end of file
build/tinyYolov2/loadQuantizedParams.d.ts 0 → 100644
import { ParamMapping } from '../commons/types';
import { NetParams } from './types';
export declare function loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: ParamMapping[];
}>;
build/tinyYolov2/loadQuantizedParams.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
var loadWeightMap_1 = require("../commons/loadWeightMap");
var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
function extractorsFactory(weightMap, paramMappings) {
var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
function extractBatchNormParams(prefix) {
var sub = extractWeightEntry(prefix + "/sub", 1);
var truediv = extractWeightEntry(prefix + "/truediv", 1);
return { sub: sub, truediv: truediv };
}
function extractConvParams(prefix) {
var filters = extractWeightEntry(prefix + "/filters", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return { filters: filters, bias: bias };
}
function extractConvWithBatchNormParams(prefix) {
var conv = extractConvParams(prefix + "/conv");
var bn = extractBatchNormParams(prefix + "/bn");
return { conv: conv, bn: bn };
}
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams
};
}
function loadQuantizedParams(uri) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, params;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
case 1:
weightMap = _b.sent();
paramMappings = [];
_a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams;
params = {
conv0: extractConvWithBatchNormParams('conv0'),
conv1: extractConvWithBatchNormParams('conv1'),
conv2: extractConvWithBatchNormParams('conv2'),
conv3: extractConvWithBatchNormParams('conv3'),
conv4: extractConvWithBatchNormParams('conv4'),
conv5: extractConvWithBatchNormParams('conv5'),
conv6: extractConvWithBatchNormParams('conv6'),
conv7: extractConvWithBatchNormParams('conv7'),
conv8: extractConvParams('conv8')
};
disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
return [2 /*return*/, { params: params, paramMappings: paramMappings }];
}
});
});
}
exports.loadQuantizedParams = loadQuantizedParams;
//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
build/tinyYolov2/loadQuantizedParams.js.map 0 → 100644
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\ No newline at end of file
build/tinyYolov2/types.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare type BatchNorm = {
sub: tf.Tensor1D;
truediv: tf.Tensor1D;
};
export declare type ConvWithBatchNorm = {
conv: ConvParams;
bn: BatchNorm;
};
export declare type NetParams = {
conv0: ConvWithBatchNorm;
conv1: ConvWithBatchNorm;
conv2: ConvWithBatchNorm;
conv3: ConvWithBatchNorm;
conv4: ConvWithBatchNorm;
conv5: ConvWithBatchNorm;
conv6: ConvWithBatchNorm;
conv7: ConvWithBatchNorm;
conv8: ConvParams;
};
export declare enum SizeType {
XS = "xs",
SM = "sm",
MD = "md",
LG = "lg",
}
export declare type TinyYolov2ForwardParams = {
inputSize?: SizeType | number;
scoreThreshold?: number;
};
build/tinyYolov2/types.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var SizeType;
(function (SizeType) {
SizeType["XS"] = "xs";
SizeType["SM"] = "sm";
SizeType["MD"] = "md";
SizeType["LG"] = "lg";
})(SizeType = exports.SizeType || (exports.SizeType = {}));
//# sourceMappingURL=types.js.map
\ No newline at end of file
build/tinyYolov2/types.js.map 0 → 100644
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\ No newline at end of file
dist/face-api.js.map
This source diff could not be displayed because it is too large. You can view the blob instead.
dist/face-api.min.js
This source diff could not be displayed because it is too large. You can view the blob instead.
examples/public/commons.js
......@@ -135,6 +135,18 @@ function renderNavBar(navbarId, exampleUri) {
name: 'MTCNN Face Recognition Webcam'
},
{
uri: 'tiny_yolov2_face_detection',
name: 'Tiny Yolov2 Face Detection'
},
{
uri: 'tiny_yolov2_face_detection_video',
name: 'Tiny Yolov2 Face Detection Video'
},
{
uri: 'tiny_yolov2_face_detection_webcam',
name: 'Tiny Yolov2 Face Detection Webcam'
},
{
uri: 'batch_face_landmarks',
name: 'Batch Face Landmarks'
},
......@@ -197,7 +209,7 @@ function renderNavBar(navbarId, exampleUri) {
})
$('.button-collapse').sideNav({
menuWidth: 250
menuWidth: 280
})
}
......
examples/server.js
......@@ -29,6 +29,9 @@ app.get('/mtcnn_face_detection_video', (req, res) => res.sendFile(path.join(view
app.get('/mtcnn_face_detection_webcam', (req, res) => res.sendFile(path.join(viewsDir, 'mtcnnFaceDetectionWebcam.html')))
app.get('/mtcnn_face_recognition', (req, res) => res.sendFile(path.join(viewsDir, 'mtcnnFaceRecognition.html')))
app.get('/mtcnn_face_recognition_webcam', (req, res) => res.sendFile(path.join(viewsDir, 'mtcnnFaceRecognitionWebcam.html')))
app.get('/tiny_yolov2_face_detection', (req, res) => res.sendFile(path.join(viewsDir, 'tinyYolov2FaceDetection.html')))
app.get('/tiny_yolov2_face_detection_video', (req, res) => res.sendFile(path.join(viewsDir, 'tinyYolov2FaceDetectionVideo.html')))
app.get('/tiny_yolov2_face_detection_webcam', (req, res) => res.sendFile(path.join(viewsDir, 'tinyYolov2FaceDetectionWebcam.html')))
app.get('/batch_face_landmarks', (req, res) => res.sendFile(path.join(viewsDir, 'batchFaceLandmarks.html')))
app.get('/batch_face_recognition', (req, res) => res.sendFile(path.join(viewsDir, 'batchFaceRecognition.html')))
......
examples/views/mtcnnFaceDetection.html
......@@ -58,7 +58,6 @@
let stage1Threshold = 0.7
let stage2Threshold = 0.7
let stage3Threshold = 0.7
let mtcnn
function onIncreaseThreshold() {
minConfidence = Math.min(faceapi.round(minConfidence + 0.1), 1.0)
......
examples/views/tinyYolov2FaceDetection.html 0 → 100644
<!DOCTYPE html>
<html>
<head>
<script src="face-api.js"></script>
<script src="commons.js"></script>
<link rel="stylesheet" href="styles.css">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
<script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
</head>
<body>
<div id="navbar"></div>
<div class="center-content page-container">
<div class="progress" id="loader">
<div class="indeterminate"></div>
</div>
<div style="position: relative" class="margin">
<img id="inputImg" src="" style="max-width: 800px;" />
<canvas id="overlay" />
</div>
<div class="row side-by-side">
<div id="selectList"></div>
<div class="row">
<label for="imgUrlInput">Get image from URL:</label>
<input id="imgUrlInput" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
onclick="loadImageFromUrl()"
>
Ok
</button>
</div>
<div class="row side-by-side">
<div class="row input-field" style="margin-right: 20px;">
<select id="sizeType">
<option value="" disabled selected>Input Size:</option>
<option value="xs">XS: 224 x 224</option>
<option value="sm">SM: 320 x 320</option>
<option value="md">MD: 416 x 416</option>
<option value="lg">LG: 608 x 608</option>
</select>
<label>Input Size</label>
</div>
<div class="row">
<label for="scoreThreshold">Score Threshold:</label>
<input disabled value="0.5" id="scoreThreshold" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
onclick="onDecreaseThreshold()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn"
onclick="onIncreaseThreshold()"
>
<i class="material-icons left">+</i>
</button>
</div>
</div>
<script>
let scoreThreshold = 0.5
let sizeType = 'lg'
function onIncreaseThreshold() {
scoreThreshold = Math.min(faceapi.round(scoreThreshold + 0.1), 1.0)
$('#scoreThreshold').val(scoreThreshold)
updateResults()
}
function onDecreaseThreshold() {
scoreThreshold = Math.max(faceapi.round(scoreThreshold - 0.1), 0.1)
$('#scoreThreshold').val(scoreThreshold)
updateResults()
}
function onSizeTypeChanged(e, c) {
sizeType = e.target.value
$('#sizeType').val(sizeType)
updateResults()
}
async function loadImageFromUrl(url) {
const img = await requestExternalImage($('#imgUrlInput').val())
$('#inputImg').get(0).src = img.src
updateResults()
}
async function updateResults() {
const inputImgEl = $('#inputImg').get(0)
const { width, height } = inputImgEl
const canvas = $('#overlay').get(0)
canvas.width = width
canvas.height = height
const forwardParams = {
inputSize: sizeType,
scoreThreshold
}
const detections = await faceapi.tinyYolov2(inputImgEl, forwardParams)
faceapi.drawDetection('overlay', detections.map(det => det.forSize(width, height)))
}
async function onSelectionChanged(uri) {
const imgBuf = await fetchImage(uri)
$(`#inputImg`).get(0).src = (await faceapi.bufferToImage(imgBuf)).src
updateResults()
}
async function run() {
await faceapi.loadTinyYolov2Model('/')
$('#loader').hide()
onSelectionChanged($('#selectList select').val())
}
$(document).ready(function() {
renderNavBar('#navbar', 'tiny_yolov2_face_detection')
renderImageSelectList(
'#selectList',
async (uri) => {
await onSelectionChanged(uri)
},
'bbt1.jpg'
)
const sizeTypeSelect = $('#sizeType')
sizeTypeSelect.val(sizeType)
sizeTypeSelect.on('change', onSizeTypeChanged)
sizeTypeSelect.material_select()
run()
})
</script>
</body>
</html>
\ No newline at end of file
examples/views/tinyYolov2FaceDetectionVideo.html 0 → 100644
<!DOCTYPE html>
<html>
<head>
<script src="face-api.js"></script>
<script src="commons.js"></script>
<link rel="stylesheet" href="styles.css">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
<script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
</head>
<body>
<div id="navbar"></div>
<div class="center-content page-container">
<div class="progress" id="loader">
<div class="indeterminate"></div>
</div>
<div style="position: relative" class="margin">
<video src="media/bbt.mp4" onplay="onPlay(this)" id="inputVideo" autoplay muted></video>
<canvas id="overlay" />
</div>
<div class="row side-by-side">
<div class="row input-field" style="margin-right: 20px;">
<select id="sizeType">
<option value="" disabled selected>Input Size:</option>
<option value="xs">XS: 224 x 224</option>
<option value="sm">SM: 320 x 320</option>
<option value="md">MD: 416 x 416</option>
<option value="lg">LG: 608 x 608</option>
</select>
<label>Input Size</label>
</div>
<div class="row">
<label for="scoreThreshold">Score Threshold:</label>
<input disabled value="0.5" id="scoreThreshold" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
onclick="onDecreaseThreshold()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn"
onclick="onIncreaseThreshold()"
>
<i class="material-icons left">+</i>
</button>
</div>
<div class="row side-by-side">
<div class="row">
<label for="time">Time:</label>
<input disabled value="-" id="time" type="text" class="bold">
</div>
<div class="row">
<label for="fps">Estimated Fps:</label>
<input disabled value="-" id="fps" type="text" class="bold">
</div>
</div>
</div>
<script>
let scoreThreshold = 0.5
let sizeType = 'md'
let modelLoaded = false
function onIncreaseThreshold() {
scoreThreshold = Math.min(faceapi.round(scoreThreshold + 0.1), 1.0)
$('#scoreThreshold').val(scoreThreshold)
}
function onDecreaseThreshold() {
scoreThreshold = Math.max(faceapi.round(scoreThreshold - 0.1), 0.1)
$('#scoreThreshold').val(scoreThreshold)
}
function onSizeTypeChanged(e, c) {
sizeType = e.target.value
$('#sizeType').val(sizeType)
}
function displayTimeStats(timeInMs) {
$('#time').val(`${timeInMs} ms`)
$('#fps').val(`${faceapi.round(1000 / timeInMs)}`)
}
async function onPlay(videoEl) {
if(videoEl.paused || videoEl.ended || !modelLoaded)
return false
const { width, height } = faceapi.getMediaDimensions(videoEl)
const canvas = $('#overlay').get(0)
canvas.width = width
canvas.height = height
const forwardParams = {
inputSize: sizeType,
scoreThreshold
}
const ts = Date.now()
result = await faceapi.tinyYolov2(videoEl, forwardParams)
displayTimeStats(Date.now() - ts)
faceapi.drawDetection('overlay', result.map(det => det.forSize(width, height)))
setTimeout(() => onPlay(videoEl))
}
async function loadNetWeights(uri) {
return new Float32Array(await (await fetch(uri)).arrayBuffer())
}
async function run() {
await faceapi.loadTinyYolov2Model('/')
modelLoaded = true
onPlay($('#inputVideo').get(0))
$('#loader').hide()
}
$(document).ready(function() {
renderNavBar('#navbar', 'tiny_yolov2_face_detection_video')
const sizeTypeSelect = $('#sizeType')
sizeTypeSelect.val(sizeType)
sizeTypeSelect.on('change', onSizeTypeChanged)
sizeTypeSelect.material_select()
run()
})
</script>
</body>
</html>
\ No newline at end of file
examples/views/tinyYolov2FaceDetectionWebcam.html 0 → 100644
<!DOCTYPE html>
<html>
<head>
<script src="face-api.js"></script>
<script src="commons.js"></script>
<link rel="stylesheet" href="styles.css">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
<script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
</head>
<body>
<div id="navbar"></div>
<div class="center-content page-container">
<div class="progress" id="loader">
<div class="indeterminate"></div>
</div>
<div style="position: relative" class="margin">
<video onplay="onPlay(this)" id="inputVideo" autoplay muted></video>
<canvas id="overlay" />
</div>
<div class="row side-by-side">
<div class="row input-field" style="margin-right: 20px;">
<select id="sizeType">
<option value="" disabled selected>Input Size:</option>
<option value="160">160 x 160</option>
<option value="224">224 x 224</option>
<option value="320">320 x 320</option>
<option value="416">416 x 416</option>
<option value="608">608 x 608</option>
</select>
<label>Input Size</label>
</div>
<div class="row">
<label for="scoreThreshold">Score Threshold:</label>
<input disabled value="0.5" id="scoreThreshold" type="text" class="bold">
</div>
<button
class="waves-effect waves-light btn"
onclick="onDecreaseThreshold()"
>
<i class="material-icons left">-</i>
</button>
<button
class="waves-effect waves-light btn"
onclick="onIncreaseThreshold()"
>
<i class="material-icons left">+</i>
</button>
</div>
<div class="row side-by-side">
<div class="row">
<label for="time">Time:</label>
<input disabled value="-" id="time" type="text" class="bold">
</div>
<div class="row">
<label for="fps">Estimated Fps:</label>
<input disabled value="-" id="fps" type="text" class="bold">
</div>
</div>
</div>
<script>
let scoreThreshold = 0.5
let sizeType = '160'
let modelLoaded = false
function onIncreaseThreshold() {
scoreThreshold = Math.min(faceapi.round(scoreThreshold + 0.1), 1.0)
$('#scoreThreshold').val(scoreThreshold)
}
function onDecreaseThreshold() {
scoreThreshold = Math.max(faceapi.round(scoreThreshold - 0.1), 0.1)
$('#scoreThreshold').val(scoreThreshold)
}
function onSizeTypeChanged(e, c) {
sizeType = e.target.value
$('#sizeType').val(sizeType)
}
function displayTimeStats(timeInMs) {
$('#time').val(`${timeInMs} ms`)
$('#fps').val(`${faceapi.round(1000 / timeInMs)}`)
}
async function onPlay(videoEl) {
if(videoEl.paused || videoEl.ended || !modelLoaded)
return false
const { width, height } = faceapi.getMediaDimensions(videoEl)
const canvas = $('#overlay').get(0)
canvas.width = width
canvas.height = height
const forwardParams = {
inputSize: parseInt(sizeType),
scoreThreshold
}
const ts = Date.now()
result = await faceapi.tinyYolov2(videoEl, forwardParams)
displayTimeStats(Date.now() - ts)
faceapi.drawDetection('overlay', result.map(det => det.forSize(width, height)))
setTimeout(() => onPlay(videoEl))
}
async function loadNetWeights(uri) {
return new Float32Array(await (await fetch(uri)).arrayBuffer())
}
async function run() {
await faceapi.loadTinyYolov2Model('/')
modelLoaded = true
const videoEl = $('#inputVideo').get(0)
navigator.getUserMedia(
{ video: {} },
stream => videoEl.srcObject = stream,
err => console.error(err)
)
onPlay($('#inputVideo').get(0))
$('#loader').hide()
}
$(document).ready(function() {
renderNavBar('#navbar', 'tiny_yolov2_face_detection_video')
const sizeTypeSelect = $('#sizeType')
sizeTypeSelect.val(sizeType)
sizeTypeSelect.on('change', onSizeTypeChanged)
sizeTypeSelect.material_select()
run()
})
</script>
</body>
</html>
\ No newline at end of file
karma.conf.js
......@@ -3,7 +3,8 @@ const dataFiles = [
'test/images/*.png',
'test/data/*.json',
'test/media/*.mp4',
'weights/**/*'
'weights/**/*',
'weights_uncompressed/**/*'
].map(pattern => ({
pattern,
watched: false,
......
src/mtcnn/BoundingBox.ts → src/BoundingBox.ts
import { Rect } from './Rect';
export class BoundingBox {
constructor(
private _left: number,
......@@ -31,7 +33,6 @@ export class BoundingBox {
return this.bottom - this.top
}
public toSquare(): BoundingBox {
let { left, top, right, bottom } = this
......@@ -98,4 +99,8 @@ export class BoundingBox {
this.bottom + (region.bottom * this.height)
).toSquare().round()
}
public toRect(): Rect {
return new Rect(this.left, this.top, this.width, this.height)
}
}
\ No newline at end of file
src/mtcnn/nms.ts → src/commons/nonMaxSuppression.ts
import { BoundingBox } from './BoundingBox';
import { BoundingBox } from '../BoundingBox';
export function nms(
export function nonMaxSuppression(
boxes: BoundingBox[],
scores: number[],
iouThreshold: number,
......
src/faceDetectionNet/loadQuantizedParams.ts
......@@ -7,7 +7,7 @@ import { loadWeightMap } from '../commons/loadWeightMap';
import { ConvParams, ParamMapping } from '../commons/types';
import { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
const DEFAULT_MODEL_NAME = 'face_detection_model'
const DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model'
function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
......
src/globalApi.ts
......@@ -10,6 +10,8 @@ import { FullFaceDescription } from './FullFaceDescription';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { NetInput } from './NetInput';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
import { TinyYolov2ForwardParams } from './tinyYolov2/types';
import { TNetInput } from './types';
export const detectionNet = new FaceDetectionNet()
......@@ -22,7 +24,8 @@ export const nets = {
ssdMobilenet: detectionNet,
faceLandmark68Net: landmarkNet,
faceRecognitionNet: recognitionNet,
mtcnn: new Mtcnn()
mtcnn: new Mtcnn(),
tinyYolov2: new TinyYolov2()
}
export function loadFaceDetectionModel(url: string) {
......@@ -41,12 +44,17 @@ export function loadMtcnnModel(url: string) {
return nets.mtcnn.load(url)
}
export function loadTinyYolov2Model(url: string) {
return nets.tinyYolov2.load(url)
}
export function loadModels(url: string) {
return Promise.all([
loadFaceDetectionModel(url),
loadFaceLandmarkModel(url),
loadFaceRecognitionModel(url),
loadMtcnnModel(url)
loadMtcnnModel(url),
loadTinyYolov2Model(url)
])
}
......@@ -77,6 +85,13 @@ export function mtcnn(
return nets.mtcnn.forward(input, forwardParams)
}
export function tinyYolov2(
input: TNetInput,
forwardParams: TinyYolov2ForwardParams
): Promise<FaceDetection[]> {
return nets.tinyYolov2.locateFaces(input, forwardParams)
}
export type allFacesFunction = (
input: tf.Tensor | NetInput | TNetInput,
minConfidence?: number,
......
src/index.ts
......@@ -21,5 +21,6 @@ export * from './faceRecognitionNet';
export * from './globalApi';
export * from './mtcnn';
export * from './padToSquare';
export * from './tinyYolov2';
export * from './toNetInput';
export * from './utils'
\ No newline at end of file
src/mtcnn/extractImagePatches.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from '../BoundingBox';
import { Dimensions } from '../types';
import { createCanvas, getContext2dOrThrow } from '../utils';
import { BoundingBox } from './BoundingBox';
import { normalize } from './normalize';
export async function extractImagePatches(
......
src/mtcnn/stage1.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from '../BoundingBox';
import { nonMaxSuppression } from '../commons/nonMaxSuppression';
import { Point } from '../Point';
import { BoundingBox } from './BoundingBox';
import { CELL_SIZE, CELL_STRIDE } from './config';
import { nms } from './nms';
import { getSizesForScale } from './getSizesForScale';
import { normalize } from './normalize';
import { PNet } from './PNet';
import { PNetParams } from './types';
import { getSizesForScale } from './getSizesForScale';
function rescaleAndNormalize(x: tf.Tensor4D, scale: number): tf.Tensor4D {
return tf.tidy(() => {
......@@ -109,7 +109,7 @@ export function stage1(
}
let ts = Date.now()
const indices = nms(
const indices = nonMaxSuppression(
boundingBoxes.map(bbox => bbox.cell),
boundingBoxes.map(bbox => bbox.score),
0.5
......@@ -130,7 +130,7 @@ export function stage1(
if (allBoxes.length > 0) {
let ts = Date.now()
const indices = nms(
const indices = nonMaxSuppression(
allBoxes.map(bbox => bbox.cell),
allBoxes.map(bbox => bbox.score),
0.7
......
src/mtcnn/stage2.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from './BoundingBox';
import { BoundingBox } from '../BoundingBox';
import { nonMaxSuppression } from '../commons/nonMaxSuppression';
import { extractImagePatches } from './extractImagePatches';
import { nms } from './nms';
import { RNet } from './RNet';
import { RNetParams } from './types';
......@@ -47,7 +47,7 @@ export async function stage2(
if (filteredBoxes.length > 0) {
ts = Date.now()
const indicesNms = nms(
const indicesNms = nonMaxSuppression(
filteredBoxes,
filteredScores,
0.7
......
src/mtcnn/stage3.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from '../BoundingBox';
import { nonMaxSuppression } from '../commons/nonMaxSuppression';
import { Point } from '../Point';
import { BoundingBox } from './BoundingBox';
import { extractImagePatches } from './extractImagePatches';
import { nms } from './nms';
import { ONet } from './ONet';
import { ONetParams } from './types';
......@@ -57,7 +57,7 @@ export async function stage3(
if (filteredBoxes.length > 0) {
ts = Date.now()
const indicesNms = nms(
const indicesNms = nonMaxSuppression(
filteredBoxes,
filteredScores,
0.7,
......
src/tinyYolov2/TinyYolov2.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox } from '../BoundingBox';
import { convLayer } from '../commons/convLayer';
import { NeuralNetwork } from '../commons/NeuralNetwork';
import { nonMaxSuppression } from '../commons/nonMaxSuppression';
import { FaceDetection } from '../FaceDetection';
import { NetInput } from '../NetInput';
import { toNetInput } from '../toNetInput';
import { TNetInput } from '../types';
import { BOX_ANCHORS, INPUT_SIZES, IOU_THRESHOLD, NUM_BOXES } from './config';
import { convWithBatchNorm } from './convWithBatchNorm';
import { extractParams } from './extractParams';
import { getDefaultParams } from './getDefaultParams';
import { loadQuantizedParams } from './loadQuantizedParams';
import { NetParams, TinyYolov2ForwardParams } from './types';
export class TinyYolov2 extends NeuralNetwork<NetParams> {
constructor() {
super('TinyYolov2')
}
public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {
const { params } = this
if (!params) {
throw new Error('TinyYolov2 - load model before inference')
}
const out = tf.tidy(() => {
const batchTensor = input.toBatchTensor(inputSize, false).div(tf.scalar(255)).toFloat() as tf.Tensor4D
let out = convWithBatchNorm(batchTensor, params.conv0)
out = tf.maxPool(out, [2, 2], [2, 2], 'same')
out = convWithBatchNorm(out, params.conv1)
out = tf.maxPool(out, [2, 2], [2, 2], 'same')
out = convWithBatchNorm(out, params.conv2)
out = tf.maxPool(out, [2, 2], [2, 2], 'same')
out = convWithBatchNorm(out, params.conv3)
out = tf.maxPool(out, [2, 2], [2, 2], 'same')
out = convWithBatchNorm(out, params.conv4)
out = tf.maxPool(out, [2, 2], [2, 2], 'same')
out = convWithBatchNorm(out, params.conv5)
out = tf.maxPool(out, [2, 2], [1, 1], 'same')
out = convWithBatchNorm(out, params.conv6)
out = convWithBatchNorm(out, params.conv7)
out = convLayer(out, params.conv8, 'valid', false)
return out
})
return out
}
public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {
return await this.forwardInput(await toNetInput(input, true, true), inputSize)
}
public async locateFaces(input: TNetInput, forwardParams: TinyYolov2ForwardParams = {}): Promise<FaceDetection[]> {
const { inputSize: _inputSize, scoreThreshold } = getDefaultParams(forwardParams)
const inputSize = typeof _inputSize === 'string'
? INPUT_SIZES[_inputSize]
: _inputSize
if (typeof inputSize !== 'number') {
throw new Error(`TinyYolov2 - unkown inputSize: ${inputSize}, expected number or one of xs | sm | md | lg`)
}
const netInput = await toNetInput(input, true)
const out = await this.forwardInput(netInput, inputSize)
const numCells = out.shape[1]
const [boxesTensor, scoresTensor] = tf.tidy(() => {
const reshaped = out.reshape([numCells, numCells, NUM_BOXES, 6])
out.dispose()
const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, NUM_BOXES, 4])
const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, NUM_BOXES, 1])
return [boxes, scores]
})
const expit = (x: number): number => 1 / (1 + Math.exp(-x))
const paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0)
const paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0)
const boxes: BoundingBox[] = []
const scores: number[] = []
for (let row = 0; row < numCells; row ++) {
for (let col = 0; col < numCells; col ++) {
for (let box = 0; box < NUM_BOXES; box ++) {
const score = expit(scoresTensor.get(row, col, box, 0))
if (score > scoreThreshold) {
const ctX = ((col + expit(boxesTensor.get(row, col, box, 0))) / numCells) * paddedWidthRelative
const ctY = ((row + expit(boxesTensor.get(row, col, box, 1))) / numCells) * paddedHeightRelative
const width = ((Math.exp(boxesTensor.get(row, col, box, 2)) * BOX_ANCHORS[box].x) / numCells) * paddedWidthRelative
const height = ((Math.exp(boxesTensor.get(row, col, box, 3)) * BOX_ANCHORS[box].y) / numCells) * paddedHeightRelative
const x = (ctX - (width / 2))
const y = (ctY - (height / 2))
boxes.push(new BoundingBox(x, y, x + width, y + height))
scores.push(score)
}
}
}
}
boxesTensor.dispose()
scoresTensor.dispose()
const indices = nonMaxSuppression(
boxes.map(box => new BoundingBox(
box.left * inputSize,
box.top * inputSize,
box.right * inputSize,
box.bottom * inputSize
)),
scores,
IOU_THRESHOLD,
true
)
const detections = indices.map(idx =>
new FaceDetection(
scores[idx],
boxes[idx].toRect(),
{ width: netInput.getInputWidth(0), height: netInput.getInputHeight(0) }
)
)
return detections
}
protected loadQuantizedParams(uri: string | undefined) {
return loadQuantizedParams(uri)
}
protected extractParams(weights: Float32Array) {
return extractParams(weights)
}
}
\ No newline at end of file
src/tinyYolov2/config.ts 0 → 100644
import { Point } from '../Point';
export const INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 }
export const NUM_BOXES = 5
export const IOU_THRESHOLD = 0.4
export const BOX_ANCHORS = [
new Point(0.738768, 0.874946),
new Point(2.42204, 2.65704),
new Point(4.30971, 7.04493),
new Point(10.246, 4.59428),
new Point(12.6868, 11.8741)
]
\ No newline at end of file
src/tinyYolov2/convWithBatchNorm.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { leaky } from './leaky';
import { ConvWithBatchNorm } from './types';
export function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {
return tf.tidy(() => {
let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D
out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')
out = tf.sub(out, params.bn.sub)
out = tf.mul(out, params.bn.truediv)
out = tf.add(out, params.conv.bias)
return leaky(out)
})
}
\ No newline at end of file
src/tinyYolov2/extractParams.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { extractConvParamsFactory } from '../commons/extractConvParamsFactory';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { ExtractWeightsFunction, ParamMapping } from '../commons/types';
import { BatchNorm, ConvWithBatchNorm, NetParams } from './types';
function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)
function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {
const sub = tf.tensor1d(extractWeights(size))
const truediv = tf.tensor1d(extractWeights(size))
paramMappings.push(
{ paramPath: `${mappedPrefix}/sub` },
{ paramPath: `${mappedPrefix}/truediv` }
)
return { sub, truediv }
}
function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {
const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)
const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)
return { conv, bn }
}
return {
extractConvParams,
extractConvWithBatchNormParams
}
}
export function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {
const {
extractWeights,
getRemainingWeights
} = extractWeightsFactory(weights)
const paramMappings: ParamMapping[] = []
const {
extractConvParams,
extractConvWithBatchNormParams
} = extractorsFactory(extractWeights, paramMappings)
const conv0 = extractConvWithBatchNormParams(3, 16, 'conv0')
const conv1 = extractConvWithBatchNormParams(16, 32, 'conv1')
const conv2 = extractConvWithBatchNormParams(32, 64, 'conv2')
const conv3 = extractConvWithBatchNormParams(64, 128, 'conv3')
const conv4 = extractConvWithBatchNormParams(128, 256, 'conv4')
const conv5 = extractConvWithBatchNormParams(256, 512, 'conv5')
const conv6 = extractConvWithBatchNormParams(512, 1024, 'conv6')
const conv7 = extractConvWithBatchNormParams(1024, 1024, 'conv7')
const conv8 = extractConvParams(1024, 30, 1, 'conv8')
if (getRemainingWeights().length !== 0) {
throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
}
const params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }
return { params, paramMappings }
}
\ No newline at end of file
src/tinyYolov2/getDefaultParams.ts 0 → 100644
import { SizeType, TinyYolov2ForwardParams } from './types';
export function getDefaultParams(params: TinyYolov2ForwardParams) {
return Object.assign(
{},
{
sizeType: SizeType.MD,
scoreThreshold: 0.5
},
params
)
}
\ No newline at end of file
src/tinyYolov2/index.ts 0 → 100644
import { TinyYolov2 } from './TinyYolov2';
export * from './TinyYolov2';
export function createTinyYolov2(weights: Float32Array) {
const net = new TinyYolov2()
net.extractWeights(weights)
return net
}
\ No newline at end of file
src/tinyYolov2/leaky.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export function leaky(x: tf.Tensor4D): tf.Tensor4D {
return tf.tidy(() => {
return tf.maximum(x, tf.mul(x, tf.scalar(0.10000000149011612)))
})
}
\ No newline at end of file
src/tinyYolov2/loadQuantizedParams.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { disposeUnusedWeightTensors } from '../commons/disposeUnusedWeightTensors';
import { extractWeightEntryFactory } from '../commons/extractWeightEntryFactory';
import { loadWeightMap } from '../commons/loadWeightMap';
import { ConvParams, ParamMapping } from '../commons/types';
import { BatchNorm, ConvWithBatchNorm, NetParams } from './types';
const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'
function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
function extractBatchNormParams(prefix: string): BatchNorm {
const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)
const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)
return { sub, truediv }
}
function extractConvParams(prefix: string): ConvParams {
const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)
const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
return { filters, bias }
}
function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {
const conv = extractConvParams(`${prefix}/conv`)
const bn = extractBatchNormParams(`${prefix}/bn`)
return { conv, bn }
}
return {
extractConvParams,
extractConvWithBatchNormParams
}
}
export async function loadQuantizedParams(
uri: string | undefined
): Promise<{ params: NetParams, paramMappings: ParamMapping[] }> {
const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
const paramMappings: ParamMapping[] = []
const {
extractConvParams,
extractConvWithBatchNormParams
} = extractorsFactory(weightMap, paramMappings)
const params = {
conv0: extractConvWithBatchNormParams('conv0'),
conv1: extractConvWithBatchNormParams('conv1'),
conv2: extractConvWithBatchNormParams('conv2'),
conv3: extractConvWithBatchNormParams('conv3'),
conv4: extractConvWithBatchNormParams('conv4'),
conv5: extractConvWithBatchNormParams('conv5'),
conv6: extractConvWithBatchNormParams('conv6'),
conv7: extractConvWithBatchNormParams('conv7'),
conv8: extractConvParams('conv8')
}
disposeUnusedWeightTensors(weightMap, paramMappings)
return { params, paramMappings }
}
\ No newline at end of file
src/tinyYolov2/types.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export type BatchNorm = {
sub: tf.Tensor1D
truediv: tf.Tensor1D
}
export type ConvWithBatchNorm = {
conv: ConvParams
bn: BatchNorm
}
export type NetParams = {
conv0: ConvWithBatchNorm
conv1: ConvWithBatchNorm
conv2: ConvWithBatchNorm
conv3: ConvWithBatchNorm
conv4: ConvWithBatchNorm
conv5: ConvWithBatchNorm
conv6: ConvWithBatchNorm
conv7: ConvWithBatchNorm
conv8: ConvParams
}
export enum SizeType {
XS = 'xs',
SM = 'sm',
MD = 'md',
LG = 'lg'
}
export type TinyYolov2ForwardParams = {
inputSize?: SizeType | number
scoreThreshold?: number
}
\ No newline at end of file
test/tests/e2e/expectedResults.ts
import * as faceapi from '../../../src';
import { FaceLandmarks5 } from '../../../src/mtcnn/FaceLandmarks5';
import { Point } from '../../../src/Point';
import { expectMaxDelta, expectPointClose, expectRectClose } from '../../utils';
......@@ -21,6 +20,15 @@ export const expectedMtcnnBoxes = [
{ x: 451, y: 176, width: 122, height: 122 }
]
export const expectedTinyYolov2Boxes = [
{ x: 52, y: 263, width: 106, height: 102 },
{ x: 455, y: 191, width: 103, height: 97 },
{ x: 236, y: 57, width: 90, height: 85 },
{ x: 257, y: 243, width: 86, height: 95 },
{ x: 578, y: 76, width: 86, height: 91 },
{ x: 87, y: 30, width: 92, height: 93 }
]
export const expectedMtcnnFaceLandmarks = [
[new Point(117, 58), new Point(156, 63), new Point(141, 86), new Point(109, 98), new Point(147, 104)],
[new Point(82, 292), new Point(134, 304), new Point(104, 330), new Point(72, 342), new Point(120, 353)],
......
test/tests/e2e/faceDetectionNet.test.ts
......@@ -79,7 +79,7 @@ describe('faceDetectionNet', () => {
it('disposes all param tensors', async () => {
await expectAllTensorsReleased(async () => {
const res = await fetch('base/weights/uncompressed/face_detection_model.weights')
const res = await fetch('base/weights_uncompressed/ssd_mobilenetv1_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
const net = faceapi.createFaceDetectionNet(weights)
net.dispose()
......
test/tests/e2e/faceLandmarkNet.test.ts
......@@ -211,7 +211,7 @@ describe('faceLandmarkNet', () => {
it('disposes all param tensors', async () => {
await expectAllTensorsReleased(async () => {
const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
const res = await fetch('base/weights_uncompressed/face_landmark_68_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
const net = faceapi.createFaceLandmarkNet(weights)
net.dispose()
......
test/tests/e2e/faceRecognitionNet.test.ts
......@@ -139,7 +139,7 @@ describe('faceRecognitionNet', () => {
it('disposes all param tensors', async () => {
await expectAllTensorsReleased(async () => {
const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
const res = await fetch('base/weights_uncompressed/face_recognition_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
const net = faceapi.createFaceRecognitionNet(weights)
net.dispose()
......
test/tests/e2e/mtcnn.test.ts
......@@ -67,7 +67,7 @@ describe('mtcnn', () => {
it('disposes all param tensors', async () => {
await expectAllTensorsReleased(async () => {
const res = await fetch('base/weights/uncompressed/mtcnn_model.weights')
const res = await fetch('base/weights_uncompressed/mtcnn_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
const net = faceapi.createMtcnn(weights)
net.dispose()
......
test/tests/e2e/tinyYolov2.test.ts 0 → 100644
import * as faceapi from '../../../src';
import { SizeType } from '../../../src/tinyYolov2/types';
import { describeWithNets, expectAllTensorsReleased, expectRectClose } from '../../utils';
import { expectedTinyYolov2Boxes } from './expectedResults';
describe('tinyYolov2', () => {
let imgEl: HTMLImageElement
beforeAll(async () => {
const img = await (await fetch('base/test/images/faces.jpg')).blob()
imgEl = await faceapi.bufferToImage(img)
})
describeWithNets('quantized weights', { withTinyYolov2: { quantized: true } }, ({ tinyYolov2 }) => {
it('inputSize lg, finds all faces', async () => {
const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: SizeType.LG })
const expectedScores = [0.86, 0.86, 0.85, 0.83, 0.81, 0.81]
const maxBoxDelta = 3
const boxOrder = [0, 1, 2, 3, 4, 5]
expect(detections.length).toEqual(6)
detections.forEach((det, i) => {
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
})
})
it('inputSize md, finds all faces', async () => {
const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: SizeType.MD })
const expectedScores = [0.89, 0.87, 0.83, 0.82, 0.81, 0.72]
const maxBoxDelta = 16
const boxOrder = [5, 4, 0, 2, 1, 3]
expect(detections.length).toEqual(6)
detections.forEach((det, i) => {
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
})
})
it('inputSize custom, finds all faces', async () => {
const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
const expectedScores = [0.89, 0.87, 0.83, 0.82, 0.81, 0.72]
const maxBoxDelta = 16
const boxOrder = [5, 4, 0, 2, 1, 3]
expect(detections.length).toEqual(6)
detections.forEach((det, i) => {
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
})
})
})
describeWithNets('uncompressed weights', { withTinyYolov2: { quantized: false } }, ({ tinyYolov2 }) => {
it('inputSize lg, finds all faces', async () => {
const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: SizeType.LG })
const expectedScores = [0.86, 0.86, 0.85, 0.83, 0.81, 0.81]
const maxBoxDelta = 1
const boxOrder = [0, 1, 2, 3, 4, 5]
expect(detections.length).toEqual(6)
detections.forEach((det, i) => {
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
})
})
it('inputSize md, finds all faces', async () => {
const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: SizeType.MD })
const expectedScores = [0.89, 0.87, 0.83, 0.83, 0.81, 0.73]
const maxBoxDelta = 14
const boxOrder = [5, 4, 2, 0, 1, 3]
expect(detections.length).toEqual(6)
detections.forEach((det, i) => {
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
})
})
it('inputSize custom, finds all faces', async () => {
const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
const expectedScores = [0.89, 0.87, 0.83, 0.83, 0.81, 0.73]
const maxBoxDelta = 14
const boxOrder = [5, 4, 2, 0, 1, 3]
expect(detections.length).toEqual(6)
detections.forEach((det, i) => {
expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
})
})
})
describe('no memory leaks', () => {
describe('NeuralNetwork, uncompressed model', () => {
it('disposes all param tensors', async () => {
await expectAllTensorsReleased(async () => {
const res = await fetch('base/weights_uncompressed/tiny_yolov2_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
const net = faceapi.createTinyYolov2(weights)
net.dispose()
})
})
})
describe('NeuralNetwork, quantized model', () => {
it('disposes all param tensors', async () => {
await expectAllTensorsReleased(async () => {
const net = new faceapi.TinyYolov2()
await net.load('base/weights')
net.dispose()
})
})
})
})
})
\ No newline at end of file
test/utils.ts
......@@ -5,7 +5,8 @@ import * as faceapi from '../src/';
import { NeuralNetwork } from '../src/commons/NeuralNetwork';
import { IPoint } from '../src/';
import { allFacesFactory, allFacesMtcnnFactory } from '../src/allFacesFactory';
import { allFacesMtcnnFunction, allFacesFunction } from '../src/globalApi';
import { allFacesMtcnnFunction, allFacesFunction, tinyYolov2 } from '../src/globalApi';
import { TinyYolov2 } from '../src/tinyYolov2/TinyYolov2';
export function zeros(length: number): Float32Array {
return new Float32Array(length)
......@@ -55,12 +56,13 @@ export type WithNetOptions = {
}
export type InjectNetArgs = {
allFaces: allFacesFunction
allFacesMtcnn: allFacesMtcnnFunction
faceDetectionNet: faceapi.FaceDetectionNet
faceLandmarkNet: faceapi.FaceLandmarkNet
faceRecognitionNet: faceapi.FaceRecognitionNet
mtcnn: faceapi.Mtcnn
allFaces: allFacesFunction
allFacesMtcnn: allFacesMtcnnFunction
tinyYolov2: faceapi.TinyYolov2
}
......@@ -71,6 +73,7 @@ export type DescribeWithNetsOptions = {
withFaceLandmarkNet?: WithNetOptions
withFaceRecognitionNet?: WithNetOptions
withMtcnn?: WithNetOptions
withTinyYolov2?: WithNetOptions
}
async function loadNetWeights(uri: string): Promise<Float32Array> {
......@@ -83,7 +86,7 @@ async function initNet<TNet extends NeuralNetwork<any>>(
) {
await net.load(
uncompressedFilename
? await loadNetWeights(`base/weights/uncompressed/${uncompressedFilename}`)
? await loadNetWeights(`base/weights_uncompressed/${uncompressedFilename}`)
: 'base/weights'
)
}
......@@ -99,25 +102,28 @@ export function describeWithNets(
let faceLandmarkNet: faceapi.FaceLandmarkNet = new faceapi.FaceLandmarkNet()
let faceRecognitionNet: faceapi.FaceRecognitionNet = new faceapi.FaceRecognitionNet()
let mtcnn: faceapi.Mtcnn = new faceapi.Mtcnn()
let tinyYolov2: faceapi.TinyYolov2 = new faceapi.TinyYolov2()
let allFaces = allFacesFactory(faceDetectionNet, faceLandmarkNet, faceRecognitionNet)
let allFacesMtcnn = allFacesMtcnnFactory(mtcnn, faceRecognitionNet)
beforeAll(async () => {
const {
withAllFaces,
withAllFacesMtcnn,
withFaceDetectionNet,
withFaceLandmarkNet,
withFaceRecognitionNet,
withMtcnn,
withAllFaces,
withAllFacesMtcnn
withTinyYolov2
} = options
if (withFaceDetectionNet || withAllFaces) {
await initNet<faceapi.FaceDetectionNet>(
faceDetectionNet,
!!withFaceDetectionNet && !withFaceDetectionNet.quantized && 'face_detection_model.weights'
!!withFaceDetectionNet && !withFaceDetectionNet.quantized && 'ssd_mobilenetv1_model.weights'
)
}
if (withFaceLandmarkNet || withAllFaces) {
await initNet<faceapi.FaceLandmarkNet>(
faceLandmarkNet,
......@@ -132,22 +138,31 @@ export function describeWithNets(
'face_recognition_model.weights'
)
}
if (withMtcnn || withAllFacesMtcnn) {
await initNet<faceapi.Mtcnn>(
mtcnn,
!!withMtcnn && !withMtcnn.quantized && 'mtcnn_model.weights'
)
}
if (withTinyYolov2) {
await initNet<faceapi.TinyYolov2>(
tinyYolov2,
!!withTinyYolov2 && !withTinyYolov2.quantized && 'tiny_yolov2_model.weights'
)
}
})
afterAll(() => {
faceDetectionNet && faceDetectionNet.dispose()
faceLandmarkNet && faceLandmarkNet.dispose()
faceRecognitionNet && faceRecognitionNet.dispose()
mtcnn && mtcnn.dispose()
mtcnn && mtcnn.dispose(),
tinyYolov2 && tinyYolov2.dispose()
})
specDefinitions({ faceDetectionNet, faceLandmarkNet, faceRecognitionNet, mtcnn, allFaces, allFacesMtcnn })
specDefinitions({ allFaces, allFacesMtcnn, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, mtcnn, tinyYolov2 })
})
}
tools/quantize/package.json 0 → 100644
{
"scripts": {
"start": "node server.js"
},
"author": "justadudewhohacks",
"license": "MIT",
"dependencies": {
"express": "^4.16.3",
"file-saver": "^1.3.8"
}
}
tools/quantize/public/quantization.js 0 → 100644
function getQuantizationRange(min, max, qdtype) {
if (qdtype !== 0 && qdtype !== 1) {
throw new Error('qdtype !== 0 && qdtype !== 1: ' + qdtype)
}
const quantMax = qdtype === 0 ? 255 : 65535
const scale = (max - min) / quantMax
let result = { scale, min, max }
if (min <= 0 && 0 <= max) {
const quantizedZeroPoint = (0 - min) / scale
const nudgedZeroPoint = Math.round(quantizedZeroPoint)
result.min = (-nudgedZeroPoint) * scale
result.max = quantMax * scale + result.min
}
return result
}
function quantizeWeights(tensor, qdtype = 0) {
const min = tensor.min().dataSync()[0]
const max = tensor.max().dataSync()[0]
if (min === max) {
return {
scale: 1.0,
min,
qdata: qdtype === 0 ? new Uint8Array(tensor.size) : new Uint16Array(tensor.size)
}
}
const q = getQuantizationRange(min, max, qdtype)
const qdata = tf.round(tf.clipByValue(tensor, q.min, q.max).sub(tf.scalar(q.min)).div(tf.scalar(q.scale))).dataSync()
return {
scale: q.scale,
min: q.min,
qdata: qdtype === 0 ? new Uint8Array(qdata) : new Uint16Array(qdata)
}
}
function dequantizeWeights(qdata, scale, min) {
if (qdata.qdata && qdata.scale && qdata.min) {
return Float32Array.from(qdata.qdata, v => (v * qdata.scale) + qdata.min)
}
return Float32Array.from(qdata, v => (v * scale) + min)
}
tools/quantize/server.js 0 → 100644
const express = require('express')
const path = require('path')
const app = express()
const viewsDir = path.join(__dirname, 'views')
app.use(express.static(viewsDir))
app.use(express.static(path.join(__dirname, './public')))
app.use(express.static(path.join(__dirname, './node_modules/file-saver')))
app.use(express.static(path.join(__dirname, '../../examples/public')))
app.use(express.static(path.join(__dirname, '../../weights')))
app.use(express.static(path.join(__dirname, '../../dist')))
app.get('/', (req, res) => res.redirect('/quantize_tiny_yolov2'))
app.get('/quantize_tiny_yolov2', (req, res) => res.sendFile(path.join(viewsDir, 'quantizeTinyYolov2.html')))
app.listen(3000, () => console.log('Listening on port 3000!'))
\ No newline at end of file
tools/quantize/views/quantizeTinyYolov2.html 0 → 100644
<!DOCTYPE html>
<html>
<head>
<script src="face-api.js"></script>
<script src="commons.js"></script>
<script src="FileSaver.js"></script>
<script src="quantization.js"></script>
</head>
<body>
<script>
tf = faceapi.tf
async function loadNetWeights(uri) {
return new Float32Array(await (await fetch(uri)).arrayBuffer())
}
function makeShards(weightArray) {
const maxLength = 4096 * 1024
const shards = []
let shardIdx = 0
for (let i = 0; i < weightArray.length; i++) {
if (!shards[shardIdx]) {
shards[shardIdx] = []
}
shards[shardIdx].push(weightArray[i])
if (shards[shardIdx].length >= maxLength) {
shardIdx += 1
}
}
return shards.map(shardArray => new Uint8Array(shardArray))
}
async function quantizeAndSave() {
const modelName = 'tiny_yolov2'
const tinyYolov2 = new faceapi.TinyYolov2()
await tinyYolov2.load(await loadNetWeights(`uncompressed/tiny_yolov2_model.weights`))
const quantizedTensorArrays = []
const weightEntries = []
tinyYolov2.getParamList().forEach(({ path, tensor }) => {
const { scale, min, qdata } = quantizeWeights(tensor)
const weightEntry = {
name : path,
shape: tensor.shape,
dtype: tensor.dtype,
quantization: { dtype: 'uint8', scale, min }
}
console.log({ scale, min })
quantizedTensorArrays.push(qdata)
weightEntries.push(weightEntry)
})
const quantizedWeights = quantizedTensorArrays
.map(typedArray => Array.from(typedArray))
.reduce((flat, arr) => flat.concat(arr))
const shards = makeShards(quantizedWeights)
console.log('num shards: ', shards.length)
const paths = []
shards.forEach((shardData, i) => {
const shardName = `${modelName}_model-shard${i + 1}`
paths.push(shardName)
saveAs(new Blob([shardData]), shardName)
})
const weightManifest = [{
weights: weightEntries,
paths
}]
saveAs(new Blob([JSON.stringify(weightManifest)]), `${modelName}_model-weights_manifest.json`)
}
</script>
</body>
</html>
\ No newline at end of file
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