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re-added ssd_mobilenet

pull/13/head
Vladimir Mandic 2020-08-25 18:24:48 -04:00
parent 8a86d4d626
commit 24470acb18
131 changed files with 3178 additions and 39786 deletions
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.gitattributes vendored Normal file
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weights/** filter=lfs diff=lfs merge=lfs -text

33
README.md
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@ -22,13 +22,19 @@ If original repository is updated, this one will become obsolete.
- Updated dependencies to @tensorflow/tfjs since backends were removed from @tensorflow/tfjs-core
- Removed following models as they are either obsolete or non-functional with tfjs 2.0+
- ssdMobilenetv1: Relies on batchNorm() function which is made obsolete in tfjs 2.0
- mtcnn: Mostly obsolete
- tinyYolov2: Non-functional since weights are missing
Which means the only valid model is **tinyFaceDetector**
- Updated newer version for mobileNetv1 model from <https://github.com/yeephycho/tensorflow-face-detection>
Note that updated model was in TF Frozen format and needed to be converted to TFJS Graph format
Due to reduced code and changed build process, resulting bundle is about **2.5x smaller** than the original!
Which means valid models are **tinyFaceDetector** and **mobileNetv1**
Due to reduced code and changed build process, resulting bundle is about **2x smaller** than the original!
## Weights
Pretrained models are includes in `./weights` and uplodaed using GIT LFS support.
## Build
@ -44,3 +50,24 @@ Which will compile everything in `./src` into `./build` and create both standard
## Documentation
For documentation refer to original project
## Todo
face-api.js mobilenetv1 model actuall comes from <https://github.com/yeephycho/tensorflow-face-detection> which has been updated for tfjs@2.0 compatibility.
unfortunately, model provided at <https://drive.google.com/open?id=0B5ttP5kO_loUdWZWZVVrN2VmWFk> is in frozen format, so needed to convert it to tfjs graph model:
> summarize_graph \
--in_graph="./frozen_inference_graph_face.pb" \
--print_structure=false
Found 4 possible outputs: (name=detection_boxes, op=Identity) (name=detection_scores, op=Identity) (name=detection_classes, op=Identity) (name=num_detections, op=Identity)
> tensorflowjs_converter \
--input_format tf_frozen_model \
--output_format tfjs_graph_model \
--skip_op_check \
--strip_debug_ops=True \
--weight_shard_size_bytes 4194304 \
--output_node_names detection_boxes,detection_scores,num_detections \
./frozen_inference_graph_face.pb \
./converted/
> cat converted/model.json | sed 's/group1-/ssd_mobilenetv1_model-/g' > converted/ssd_mobilenetv1_model-weights_manifest.json
> rm converted/model.json

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2
build/dom/loadWeightMap.d.ts.map
View File

@ -1 +1 @@
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{"version":3,"file":"loadWeightMap.d.ts","sourceRoot":"","sources":["../../src/dom/loadWeightMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAK5C,wBAAsB,aAAa,CACjC,GAAG,EAAE,MAAM,GAAG,SAAS,EACvB,gBAAgB,EAAE,MAAM,GACvB,OAAO,CAAC,EAAE,CAAC,cAAc,CAAC,CAK5B"}

1
build/dom/loadWeightMap.js
View File

@ -4,6 +4,7 @@ import { fetchJson } from './fetchJson';
export async function loadWeightMap(uri, defaultModelName) {
const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);
const manifest = await fetchJson(manifestUri);
console.log(typeof manifest, manifest);
return tf.io.loadWeights(manifest, modelBaseUri);
}
//# sourceMappingURL=loadWeightMap.js.map

2
build/dom/loadWeightMap.js.map
View File

@ -1 +1 @@
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2
build/euclideanDistance.d.ts.map
View File

@ -1 +1 @@
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{"version":3,"file":"euclideanDistance.d.ts","sourceRoot":"","sources":["../src/euclideanDistance.ts"],"names":[],"mappings":"AAAA,wBAAgB,iBAAiB,CAAC,IAAI,EAAE,MAAM,EAAE,GAAG,YAAY,EAAE,IAAI,EAAE,MAAM,EAAE,GAAG,YAAY,UAY7F"}

3
build/euclideanDistance.js
View File

@ -1,7 +1,6 @@
export function euclideanDistance(arr1, arr2) {
if (arr1.length !== arr2.length) {
if (arr1.length !== arr2.length)
throw new Error('euclideanDistance: arr1.length !== arr2.length');
}
const desc1 = Array.from(arr1);
const desc2 = Array.from(arr2);
return Math.sqrt(desc1

2
build/euclideanDistance.js.map
View File

@ -1 +1 @@
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2
build/globalApi/DetectFacesTasks.d.ts.map
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@ -1 +1 @@
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10
build/globalApi/DetectFacesTasks.js
View File

@ -1,12 +1,14 @@
import { extendWithFaceDetection } from '../factories/WithFaceDetection';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
import { TinyYolov2Options } from '../tinyYolov2';
import { ComposableTask } from './ComposableTask';
import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
import { nets } from './nets';
import { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';
import { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';
export class DetectFacesTaskBase extends ComposableTask {
constructor(input, options = new TinyFaceDetectorOptions()) {
constructor(input, options = new SsdMobilenetv1Options()) {
super();
this.input = input;
this.options = options;
@ -17,7 +19,11 @@ export class DetectAllFacesTask extends DetectFacesTaskBase {
const { input, options } = this;
const faceDetectionFunction = options instanceof TinyFaceDetectorOptions
? (input) => nets.tinyFaceDetector.locateFaces(input, options)
: null;
: (options instanceof SsdMobilenetv1Options
? (input) => nets.ssdMobilenetv1.locateFaces(input, options)
: (options instanceof TinyYolov2Options
? (input) => nets.tinyYolov2.locateFaces(input, options)
: null));
if (!faceDetectionFunction) {
throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');
}

2
build/globalApi/DetectFacesTasks.js.map
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@ -1 +1 @@
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4
build/globalApi/allFaces.d.ts vendored
View File

@ -1,5 +1,7 @@
import { TNetInput } from '../dom';
import { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';
import { ITinyYolov2Options } from '../tinyYolov2';
export declare function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]>;
export declare function allFacesTinyYolov2(input: TNetInput, forwardParams?: ITinyYolov2Options): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]>;
export declare const allFaces: typeof allFacesTinyYolov2;
export declare const allFaces: typeof allFacesSsdMobilenetv1;
//# sourceMappingURL=allFaces.d.ts.map

1
build/globalApi/allFaces.d.ts.map Normal file
View File

@ -0,0 +1 @@
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9
build/globalApi/allFaces.js
View File

@ -1,11 +1,18 @@
import { SsdMobilenetv1Options } from '../ssdMobilenetv1';
import { TinyYolov2Options } from '../tinyYolov2';
import { detectAllFaces } from './detectFaces';
// export allFaces API for backward compatibility
export async function allFacesSsdMobilenetv1(input, minConfidence) {
console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead');
return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))
.withFaceLandmarks()
.withFaceDescriptors();
}
export async function allFacesTinyYolov2(input, forwardParams = {}) {
console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead');
return await detectAllFaces(input, new TinyYolov2Options(forwardParams))
.withFaceLandmarks()
.withFaceDescriptors();
}
export const allFaces = allFacesTinyYolov2;
export const allFaces = allFacesSsdMobilenetv1;
//# sourceMappingURL=allFaces.js.map

2
build/globalApi/allFaces.js.map
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2
build/globalApi/detectFaces.d.ts.map
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6
build/globalApi/detectFaces.js
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@ -1,9 +1,9 @@
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
export function detectSingleFace(input, options = new TinyFaceDetectorOptions()) {
export function detectSingleFace(input, options = new SsdMobilenetv1Options()) {
return new DetectSingleFaceTask(input, options);
}
export function detectAllFaces(input, options = new TinyFaceDetectorOptions()) {
export function detectAllFaces(input, options = new SsdMobilenetv1Options()) {
return new DetectAllFacesTask(input, options);
}
//# sourceMappingURL=detectFaces.js.map

2
build/globalApi/detectFaces.js.map
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@ -1 +1 @@
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1
build/globalApi/index.d.ts vendored
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@ -1,3 +1,4 @@
export * from './allFaces';
export * from './ComposableTask';
export * from './ComputeFaceDescriptorsTasks';
export * from './detectFaces';

2
build/globalApi/index.d.ts.map
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@ -1 +1 @@
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1
build/globalApi/index.js
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@ -1,3 +1,4 @@
export * from './allFaces';
export * from './ComposableTask';
export * from './ComputeFaceDescriptorsTasks';
export * from './detectFaces';

2
build/globalApi/index.js.map
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@ -1 +1 @@
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24
build/globalApi/nets.d.ts vendored
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@ -8,10 +8,13 @@ import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';
import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
import { TinyYolov2 } from '../tinyYolov2';
import { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';
export declare const nets: {
ssdMobilenetv1: SsdMobilenetv1;
tinyFaceDetector: TinyFaceDetector;
tinyYolov2: TinyYolov2;
faceLandmark68Net: FaceLandmark68Net;
@ -20,6 +23,14 @@ export declare const nets: {
faceExpressionNet: FaceExpressionNet;
ageGenderNet: AgeGenderNet;
};
/**
* Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
*
* @param input The input image.
* @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export declare const ssdMobilenetv1: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
/**
* Attempts to detect all faces in an image using the Tiny Face Detector.
*
@ -28,6 +39,14 @@ export declare const nets: {
* @returns Bounding box of each face with score.
*/
export declare const tinyFaceDetector: (input: TNetInput, options: TinyFaceDetectorOptions) => Promise<FaceDetection[]>;
/**
* Attempts to detect all faces in an image using the Tiny Yolov2 Network.
*
* @param input The input image.
* @param options (optional, default: see TinyYolov2Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export declare const tinyYolov2: (input: TNetInput, options: ITinyYolov2Options) => Promise<FaceDetection[]>;
/**
* Detects the 68 point face landmark positions of the face shown in an image.
*
@ -73,6 +92,7 @@ export declare const recognizeFaceExpressions: (input: TNetInput) => Promise<Fac
* @returns Predictions with age, gender and gender probability or array thereof in case of batch input.
*/
export declare const predictAgeAndGender: (input: TNetInput) => Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]>;
export declare const loadSsdMobilenetv1Model: (url: string) => Promise<void>;
export declare const loadTinyFaceDetectorModel: (url: string) => Promise<void>;
export declare const loadTinyYolov2Model: (url: string) => Promise<void>;
export declare const loadFaceLandmarkModel: (url: string) => Promise<void>;
@ -81,6 +101,6 @@ export declare const loadFaceRecognitionModel: (url: string) => Promise<void>;
export declare const loadFaceExpressionModel: (url: string) => Promise<void>;
export declare const loadAgeGenderModel: (url: string) => Promise<void>;
export declare const loadFaceDetectionModel: (url: string) => Promise<void>;
export declare const locateFaces: typeof TinyFaceDetector;
export declare const locateFaces: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
export declare const detectLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
//# sourceMappingURL=nets.d.ts.map

2
build/globalApi/nets.d.ts.map
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@ -1 +1 @@
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23
build/globalApi/nets.js
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@ -3,9 +3,11 @@ import { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';
import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
import { TinyYolov2 } from '../tinyYolov2';
export const nets = {
ssdMobilenetv1: new SsdMobilenetv1(),
tinyFaceDetector: new TinyFaceDetector(),
tinyYolov2: new TinyYolov2(),
faceLandmark68Net: new FaceLandmark68Net(),
@ -14,6 +16,14 @@ export const nets = {
faceExpressionNet: new FaceExpressionNet(),
ageGenderNet: new AgeGenderNet()
};
/**
* Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
*
* @param input The input image.
* @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export const ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options);
/**
* Attempts to detect all faces in an image using the Tiny Face Detector.
*
@ -22,6 +32,14 @@ export const nets = {
* @returns Bounding box of each face with score.
*/
export const tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options);
/**
* Attempts to detect all faces in an image using the Tiny Yolov2 Network.
*
* @param input The input image.
* @param options (optional, default: see TinyYolov2Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export const tinyYolov2 = (input, options) => nets.tinyYolov2.locateFaces(input, options);
/**
* Detects the 68 point face landmark positions of the face shown in an image.
*
@ -67,6 +85,7 @@ export const recognizeFaceExpressions = (input) => nets.faceExpressionNet.predic
* @returns Predictions with age, gender and gender probability or array thereof in case of batch input.
*/
export const predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input);
export const loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url);
export const loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url);
export const loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url);
export const loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url);
@ -75,7 +94,7 @@ export const loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(ur
export const loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url);
export const loadAgeGenderModel = (url) => nets.ageGenderNet.load(url);
// backward compatibility
export const loadFaceDetectionModel = loadTinyFaceDetectorModel;
export const locateFaces = TinyFaceDetector;
export const loadFaceDetectionModel = loadSsdMobilenetv1Model;
export const locateFaces = ssdMobilenetv1;
export const detectLandmarks = detectFaceLandmarks;
//# sourceMappingURL=nets.js.map

2
build/globalApi/nets.js.map
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@ -1 +1 @@
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4
build/globalApi/types.d.ts vendored
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@ -1,6 +1,8 @@
import { FaceDetection } from '../classes/FaceDetection';
import { TNetInput } from '../dom';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
export declare type FaceDetectionOptions = TinyFaceDetectorOptions;
import { TinyYolov2Options } from '../tinyYolov2';
export declare type FaceDetectionOptions = TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options;
export declare type FaceDetectionFunction = (input: TNetInput) => Promise<FaceDetection[]>;
//# sourceMappingURL=types.d.ts.map

2
build/globalApi/types.d.ts.map
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@ -1 +1 @@
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3
build/index.d.ts vendored
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@ -1,4 +1,4 @@
import * as tf from '@tensorflow/tfjs';
import * as tf from '@tensorflow/tfjs-core';
import * as draw from './draw';
import * as utils from './utils';
export { draw, utils, tf };
@ -12,6 +12,7 @@ export * from './faceRecognitionNet/index';
export * from './factories/index';
export * from './globalApi/index';
export * from './ops/index';
export * from './ssdMobilenetv1/index';
export * from './tinyFaceDetector/index';
export * from './tinyYolov2/index';
export * from './euclideanDistance';

2
build/index.d.ts.map
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@ -1 +1 @@
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3
build/index.js
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@ -1,4 +1,4 @@
import * as tf from '@tensorflow/tfjs';
import * as tf from '@tensorflow/tfjs-core';
import * as draw from './draw';
import * as utils from './utils';
export { draw, utils, tf };
@ -12,6 +12,7 @@ export * from './faceRecognitionNet/index';
export * from './factories/index';
export * from './globalApi/index';
export * from './ops/index';
export * from './ssdMobilenetv1/index';
export * from './tinyFaceDetector/index';
export * from './tinyYolov2/index';
export * from './euclideanDistance';

2
build/index.js.map
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@ -1 +1 @@
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2
build/resizeResults.d.ts.map
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@ -1 +1 @@
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2
build/resizeResults.js.map
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build/ssdMobilenetv1/SsdMobilenetv1.d.ts vendored Normal file
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import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from '../classes/FaceDetection';
import { NetInput, TNetInput } from '../dom';
import { NeuralNetwork } from '../NeuralNetwork';
import { ISsdMobilenetv1Options } from './SsdMobilenetv1Options';
import { NetParams } from './types';
export declare class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
constructor();
forwardInput(input: NetInput): {
boxes: tf.Tensor2D[];
scores: tf.Tensor1D[];
};
forward(input: TNetInput): Promise<{
boxes: tf.Tensor2D[];
scores: tf.Tensor1D[];
}>;
locateFaces(input: TNetInput, options?: ISsdMobilenetv1Options): Promise<FaceDetection[]>;
protected getDefaultModelName(): string;
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: import("../common").ParamMapping[];
};
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: import("../common").ParamMapping[];
};
}
//# sourceMappingURL=SsdMobilenetv1.d.ts.map

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import * as tf from '@tensorflow/tfjs-core';
import { Rect } from '../classes';
import { FaceDetection } from '../classes/FaceDetection';
import { toNetInput } from '../dom';
import { NeuralNetwork } from '../NeuralNetwork';
import { extractParams } from './extractParams';
import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { SsdMobilenetv1Options } from './SsdMobilenetv1Options';
export class SsdMobilenetv1 extends NeuralNetwork {
constructor() {
super('SsdMobilenetv1');
}
forwardInput(input) {
const { params } = this;
if (!params) {
throw new Error('SsdMobilenetv1 - load model before inference');
}
return tf.tidy(() => {
const batchTensor = input.toBatchTensor(512, false).toFloat();
const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
const features = mobileNetV1(x, params.mobilenetv1);
const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);
return outputLayer(boxPredictions, classPredictions, params.output_layer);
});
}
async forward(input) {
return this.forwardInput(await toNetInput(input));
}
async locateFaces(input, options = {}) {
const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);
const netInput = await toNetInput(input);
const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);
// TODO batches
const boxes = _boxes[0];
const scores = _scores[0];
for (let i = 1; i < _boxes.length; i++) {
_boxes[i].dispose();
_scores[i].dispose();
}
// TODO find a better way to filter by minConfidence
const scoresData = Array.from(await scores.data());
const iouThreshold = 0.5;
const indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
const reshapedDims = netInput.getReshapedInputDimensions(0);
const inputSize = netInput.inputSize;
const padX = inputSize / reshapedDims.width;
const padY = inputSize / reshapedDims.height;
const boxesData = boxes.arraySync();
const results = indices
.map(idx => {
const [top, bottom] = [
Math.max(0, boxesData[idx][0]),
Math.min(1.0, boxesData[idx][2])
].map(val => val * padY);
const [left, right] = [
Math.max(0, boxesData[idx][1]),
Math.min(1.0, boxesData[idx][3])
].map(val => val * padX);
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
});
});
boxes.dispose();
scores.dispose();
return results;
}
getDefaultModelName() {
return 'ssd_mobilenetv1_model';
}
extractParamsFromWeigthMap(weightMap) {
return extractParamsFromWeigthMap(weightMap);
}
extractParams(weights) {
return extractParams(weights);
}
}
//# sourceMappingURL=SsdMobilenetv1.js.map

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build/ssdMobilenetv1/SsdMobilenetv1Options.d.ts vendored Normal file
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export interface ISsdMobilenetv1Options {
minConfidence?: number;
maxResults?: number;
}
export declare class SsdMobilenetv1Options {
protected _name: string;
private _minConfidence;
private _maxResults;
constructor({ minConfidence, maxResults }?: ISsdMobilenetv1Options);
get minConfidence(): number;
get maxResults(): number;
}
//# sourceMappingURL=SsdMobilenetv1Options.d.ts.map

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export class SsdMobilenetv1Options {
constructor({ minConfidence, maxResults } = {}) {
this._name = 'SsdMobilenetv1Options';
this._minConfidence = minConfidence || 0.5;
this._maxResults = maxResults || 100;
if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {
throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);
}
if (typeof this._maxResults !== 'number') {
throw new Error(`${this._name} - expected maxResults to be a number`);
}
}
get minConfidence() { return this._minConfidence; }
get maxResults() { return this._maxResults; }
}
//# sourceMappingURL=SsdMobilenetv1Options.js.map

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build/ssdMobilenetv1/boxPredictionLayer.d.ts vendored Normal file
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import * as tf from '@tensorflow/tfjs-core';
import { BoxPredictionParams } from './types';
export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
boxPredictionEncoding: tf.Tensor<tf.Rank>;
classPrediction: tf.Tensor<tf.Rank>;
};
//# sourceMappingURL=boxPredictionLayer.d.ts.map

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import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../common';
export function boxPredictionLayer(x, params) {
return tf.tidy(() => {
const batchSize = x.shape[0];
const boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
const classPrediction = tf.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
return {
boxPredictionEncoding,
classPrediction
};
});
}
//# sourceMappingURL=boxPredictionLayer.js.map

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import { ParamMapping } from '../common';
import { NetParams } from './types';
export declare function extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: ParamMapping[];
};
//# sourceMappingURL=extractParams.d.ts.map

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import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../common';
function extractorsFactory(extractWeights, paramMappings) {
function extractDepthwiseConvParams(numChannels, mappedPrefix) {
const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
paramMappings.push({ paramPath: `${mappedPrefix}/filters` }, { paramPath: `${mappedPrefix}/batch_norm_scale` }, { paramPath: `${mappedPrefix}/batch_norm_offset` }, { paramPath: `${mappedPrefix}/batch_norm_mean` }, { paramPath: `${mappedPrefix}/batch_norm_variance` });
return {
filters,
batch_norm_scale,
batch_norm_offset,
batch_norm_mean,
batch_norm_variance
};
}
function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
const filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
const bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: `${mappedPrefix}/filters` }, { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` });
return { filters, bias };
}
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
const { filters, bias } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);
return {
filters,
batch_norm_offset: bias
};
}
function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);
const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);
return { depthwise_conv, pointwise_conv };
}
function extractMobilenetV1Params() {
const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
return {
conv_0,
conv_1,
conv_2,
conv_3,
conv_4,
conv_5,
conv_6,
conv_7,
conv_8,
conv_9,
conv_10,
conv_11,
conv_12,
conv_13
};
}
function extractPredictionLayerParams() {
const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
const box_predictor_0 = {
box_encoding_predictor: box_encoding_0_predictor,
class_predictor: class_predictor_0
};
const box_predictor_1 = {
box_encoding_predictor: box_encoding_1_predictor,
class_predictor: class_predictor_1
};
const box_predictor_2 = {
box_encoding_predictor: box_encoding_2_predictor,
class_predictor: class_predictor_2
};
const box_predictor_3 = {
box_encoding_predictor: box_encoding_3_predictor,
class_predictor: class_predictor_3
};
const box_predictor_4 = {
box_encoding_predictor: box_encoding_4_predictor,
class_predictor: class_predictor_4
};
const box_predictor_5 = {
box_encoding_predictor: box_encoding_5_predictor,
class_predictor: class_predictor_5
};
return {
conv_0,
conv_1,
conv_2,
conv_3,
conv_4,
conv_5,
conv_6,
conv_7,
box_predictor_0,
box_predictor_1,
box_predictor_2,
box_predictor_3,
box_predictor_4,
box_predictor_5
};
}
return {
extractMobilenetV1Params,
extractPredictionLayerParams
};
}
export function extractParams(weights) {
const paramMappings = [];
const { extractWeights, getRemainingWeights } = extractWeightsFactory(weights);
const { extractMobilenetV1Params, extractPredictionLayerParams } = extractorsFactory(extractWeights, paramMappings);
const mobilenetv1 = extractMobilenetV1Params();
const prediction_layer = extractPredictionLayerParams();
const extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
const output_layer = {
extra_dim
};
paramMappings.push({ paramPath: 'output_layer/extra_dim' });
if (getRemainingWeights().length !== 0) {
throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
}
return {
params: {
mobilenetv1,
prediction_layer,
output_layer
},
paramMappings
};
}
//# sourceMappingURL=extractParams.js.map

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import * as tf from '@tensorflow/tfjs-core';
import { ParamMapping } from '../common';
import { NetParams } from './types';
export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
params: NetParams;
paramMappings: ParamMapping[];
};
//# sourceMappingURL=extractParamsFromWeigthMap.d.ts.map

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import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';
import { isTensor3D } from '../utils';
function extractorsFactory(weightMap, paramMappings) {
const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);
const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);
return { filters, batch_norm_offset };
}
function extractConvPairParams(idx) {
const mappedPrefix = `mobilenetv1/conv_${idx}`;
const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;
const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;
const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;
const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);
const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);
const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);
const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);
const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);
return {
depthwise_conv: {
filters,
batch_norm_scale,
batch_norm_offset,
batch_norm_mean,
batch_norm_variance
},
pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
};
}
function extractMobilenetV1Params() {
return {
conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
conv_1: extractConvPairParams(1),
conv_2: extractConvPairParams(2),
conv_3: extractConvPairParams(3),
conv_4: extractConvPairParams(4),
conv_5: extractConvPairParams(5),
conv_6: extractConvPairParams(6),
conv_7: extractConvPairParams(7),
conv_8: extractConvPairParams(8),
conv_9: extractConvPairParams(9),
conv_10: extractConvPairParams(10),
conv_11: extractConvPairParams(11),
conv_12: extractConvPairParams(12),
conv_13: extractConvPairParams(13)
};
}
function extractConvParams(prefix, mappedPrefix) {
const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);
const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);
return { filters, bias };
}
function extractBoxPredictorParams(idx) {
const box_encoding_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, `prediction_layer/box_predictor_${idx}/box_encoding_predictor`);
const class_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`, `prediction_layer/box_predictor_${idx}/class_predictor`);
return { box_encoding_predictor, class_predictor };
}
function extractPredictionLayerParams() {
return {
conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
box_predictor_0: extractBoxPredictorParams(0),
box_predictor_1: extractBoxPredictorParams(1),
box_predictor_2: extractBoxPredictorParams(2),
box_predictor_3: extractBoxPredictorParams(3),
box_predictor_4: extractBoxPredictorParams(4),
box_predictor_5: extractBoxPredictorParams(5)
};
}
return {
extractMobilenetV1Params,
extractPredictionLayerParams
};
}
export function extractParamsFromWeigthMap(weightMap) {
const paramMappings = [];
const { extractMobilenetV1Params, extractPredictionLayerParams } = extractorsFactory(weightMap, paramMappings);
const extra_dim = weightMap['Output/extra_dim'];
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
if (!isTensor3D(extra_dim)) {
throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);
}
const params = {
mobilenetv1: extractMobilenetV1Params(),
prediction_layer: extractPredictionLayerParams(),
output_layer: {
extra_dim
}
};
disposeUnusedWeightTensors(weightMap, paramMappings);
return { params, paramMappings };
}
//# sourceMappingURL=extractParamsFromWeigthMap.js.map

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import { SsdMobilenetv1 } from './SsdMobilenetv1';
export * from './SsdMobilenetv1';
export * from './SsdMobilenetv1Options';
export declare function createSsdMobilenetv1(weights: Float32Array): SsdMobilenetv1;
export declare function createFaceDetectionNet(weights: Float32Array): SsdMobilenetv1;
export declare class FaceDetectionNet extends SsdMobilenetv1 {
}
//# sourceMappingURL=index.d.ts.map

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import { SsdMobilenetv1 } from './SsdMobilenetv1';
export * from './SsdMobilenetv1';
export * from './SsdMobilenetv1Options';
export function createSsdMobilenetv1(weights) {
const net = new SsdMobilenetv1();
net.extractWeights(weights);
return net;
}
export function createFaceDetectionNet(weights) {
return createSsdMobilenetv1(weights);
}
// alias for backward compatibily
export class FaceDetectionNet extends SsdMobilenetv1 {
}
//# sourceMappingURL=index.js.map

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build/ssdMobilenetv1/mobileNetV1.d.ts vendored Normal file
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import * as tf from '@tensorflow/tfjs-core';
import { MobileNetV1 } from './types';
export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
out: tf.Tensor4D;
conv11: any;
};
//# sourceMappingURL=mobileNetV1.d.ts.map

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build/ssdMobilenetv1/mobileNetV1.js Normal file
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import * as tf from '@tensorflow/tfjs-core';
import { pointwiseConvLayer } from './pointwiseConvLayer';
const epsilon = 0.0010000000474974513;
function depthwiseConvLayer(x, params, strides) {
return tf.tidy(() => {
let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
out = tf.batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
return tf.clipByValue(out, 0, 6);
});
}
function getStridesForLayerIdx(layerIdx) {
return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1];
}
export function mobileNetV1(x, params) {
return tf.tidy(() => {
let conv11 = null;
let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
const convPairParams = [
params.conv_1,
params.conv_2,
params.conv_3,
params.conv_4,
params.conv_5,
params.conv_6,
params.conv_7,
params.conv_8,
params.conv_9,
params.conv_10,
params.conv_11,
params.conv_12,
params.conv_13
];
convPairParams.forEach((param, i) => {
const layerIdx = i + 1;
const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
if (layerIdx === 11) {
conv11 = out;
}
});
if (conv11 === null) {
throw new Error('mobileNetV1 - output of conv layer 11 is null');
}
return {
out,
conv11: conv11
};
});
}
//# sourceMappingURL=mobileNetV1.js.map

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build/ssdMobilenetv1/nonMaxSuppression.d.ts vendored Normal file
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import * as tf from '@tensorflow/tfjs-core';
export declare function nonMaxSuppression(boxes: tf.Tensor2D, scores: number[], maxOutputSize: number, iouThreshold: number, scoreThreshold: number): number[];
//# sourceMappingURL=nonMaxSuppression.d.ts.map

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build/ssdMobilenetv1/nonMaxSuppression.js Normal file
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export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
const numBoxes = boxes.shape[0];
const outputSize = Math.min(maxOutputSize, numBoxes);
const candidates = scores
.map((score, boxIndex) => ({ score, boxIndex }))
.filter(c => c.score > scoreThreshold)
.sort((c1, c2) => c2.score - c1.score);
const suppressFunc = (x) => x <= iouThreshold ? 1 : 0;
const selected = [];
candidates.forEach(c => {
if (selected.length >= outputSize) {
return;
}
const originalScore = c.score;
for (let j = selected.length - 1; j >= 0; --j) {
const iou = IOU(boxes, c.boxIndex, selected[j]);
if (iou === 0.0) {
continue;
}
c.score *= suppressFunc(iou);
if (c.score <= scoreThreshold) {
break;
}
}
if (originalScore === c.score) {
selected.push(c.boxIndex);
}
});
return selected;
}
function IOU(boxes, i, j) {
const boxesData = boxes.arraySync();
const yminI = Math.min(boxesData[i][0], boxesData[i][2]);
const xminI = Math.min(boxesData[i][1], boxesData[i][3]);
const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);
const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);
const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);
const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);
const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);
const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);
const areaI = (ymaxI - yminI) * (xmaxI - xminI);
const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
if (areaI <= 0 || areaJ <= 0) {
return 0.0;
}
const intersectionYmin = Math.max(yminI, yminJ);
const intersectionXmin = Math.max(xminI, xminJ);
const intersectionYmax = Math.min(ymaxI, ymaxJ);
const intersectionXmax = Math.min(xmaxI, xmaxJ);
const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
Math.max(intersectionXmax - intersectionXmin, 0.0);
return intersectionArea / (areaI + areaJ - intersectionArea);
}
//# sourceMappingURL=nonMaxSuppression.js.map

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build/ssdMobilenetv1/outputLayer.d.ts vendored Normal file
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import * as tf from '@tensorflow/tfjs-core';
import { OutputLayerParams } from './types';
export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
boxes: tf.Tensor2D[];
scores: tf.Tensor1D[];
};
//# sourceMappingURL=outputLayer.d.ts.map

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import * as tf from '@tensorflow/tfjs-core';
function getCenterCoordinatesAndSizesLayer(x) {
const vec = tf.unstack(tf.transpose(x, [1, 0]));
const sizes = [
tf.sub(vec[2], vec[0]),
tf.sub(vec[3], vec[1])
];
const centers = [
tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
];
return {
sizes,
centers
};
}
function decodeBoxesLayer(x0, x1) {
const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);
const vec = tf.unstack(tf.transpose(x1, [1, 0]));
const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);
const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));
const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);
return tf.transpose(tf.stack([
tf.sub(add0_out, div0_out),
tf.sub(add1_out, div1_out),
tf.add(add0_out, div0_out),
tf.add(add1_out, div1_out)
]), [1, 0]);
}
export function outputLayer(boxPredictions, classPredictions, params) {
return tf.tidy(() => {
const batchSize = boxPredictions.shape[0];
let boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
scores = tf.reshape(scores, [batchSize, scores.shape[1]]);
const boxesByBatch = tf.unstack(boxes);
const scoresByBatch = tf.unstack(scores);
return {
boxes: boxesByBatch,
scores: scoresByBatch
};
});
}
//# sourceMappingURL=outputLayer.js.map

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build/ssdMobilenetv1/pointwiseConvLayer.d.ts vendored Normal file
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import * as tf from '@tensorflow/tfjs-core';
import { PointwiseConvParams } from './types';
export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor4D;
//# sourceMappingURL=pointwiseConvLayer.d.ts.map

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{"version":3,"file":"pointwiseConvLayer.d.ts","sourceRoot":"","sources":["../../src/ssdMobilenetv1/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAE9C,wBAAgB,kBAAkB,CAChC,CAAC,EAAE,EAAE,CAAC,QAAQ,EACd,MAAM,EAAE,mBAAmB,EAC3B,OAAO,EAAE,CAAC,MAAM,EAAE,MAAM,CAAC,eAS1B"}

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build/ssdMobilenetv1/pointwiseConvLayer.js Normal file
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import * as tf from '@tensorflow/tfjs-core';
export function pointwiseConvLayer(x, params, strides) {
return tf.tidy(() => {
let out = tf.conv2d(x, params.filters, strides, 'same');
out = tf.add(out, params.batch_norm_offset);
return tf.clipByValue(out, 0, 6);
});
}
//# sourceMappingURL=pointwiseConvLayer.js.map

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build/ssdMobilenetv1/predictionLayer.d.ts vendored Normal file
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import * as tf from '@tensorflow/tfjs-core';
import { PredictionLayerParams } from './types';
export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
boxPredictions: tf.Tensor4D;
classPredictions: tf.Tensor4D;
};
//# sourceMappingURL=predictionLayer.d.ts.map

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build/ssdMobilenetv1/predictionLayer.js Normal file
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import * as tf from '@tensorflow/tfjs-core';
import { boxPredictionLayer } from './boxPredictionLayer';
import { pointwiseConvLayer } from './pointwiseConvLayer';
export function predictionLayer(x, conv11, params) {
return tf.tidy(() => {
const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
const boxPredictions = tf.concat([
boxPrediction0.boxPredictionEncoding,
boxPrediction1.boxPredictionEncoding,
boxPrediction2.boxPredictionEncoding,
boxPrediction3.boxPredictionEncoding,
boxPrediction4.boxPredictionEncoding,
boxPrediction5.boxPredictionEncoding
], 1);
const classPredictions = tf.concat([
boxPrediction0.classPrediction,
boxPrediction1.classPrediction,
boxPrediction2.classPrediction,
boxPrediction3.classPrediction,
boxPrediction4.classPrediction,
boxPrediction5.classPrediction
], 1);
return {
boxPredictions,
classPredictions
};
});
}
//# sourceMappingURL=predictionLayer.js.map

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import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../common';
export declare type PointwiseConvParams = {
filters: tf.Tensor4D;
batch_norm_offset: tf.Tensor1D;
};
export declare namespace MobileNetV1 {
type DepthwiseConvParams = {
filters: tf.Tensor4D;
batch_norm_scale: tf.Tensor1D;
batch_norm_offset: tf.Tensor1D;
batch_norm_mean: tf.Tensor1D;
batch_norm_variance: tf.Tensor1D;
};
type ConvPairParams = {
depthwise_conv: DepthwiseConvParams;
pointwise_conv: PointwiseConvParams;
};
type Params = {
conv_0: PointwiseConvParams;
conv_1: ConvPairParams;
conv_2: ConvPairParams;
conv_3: ConvPairParams;
conv_4: ConvPairParams;
conv_5: ConvPairParams;
conv_6: ConvPairParams;
conv_7: ConvPairParams;
conv_8: ConvPairParams;
conv_9: ConvPairParams;
conv_10: ConvPairParams;
conv_11: ConvPairParams;
conv_12: ConvPairParams;
conv_13: ConvPairParams;
};
}
export declare type BoxPredictionParams = {
box_encoding_predictor: ConvParams;
class_predictor: ConvParams;
};
export declare type PredictionLayerParams = {
conv_0: PointwiseConvParams;
conv_1: PointwiseConvParams;
conv_2: PointwiseConvParams;
conv_3: PointwiseConvParams;
conv_4: PointwiseConvParams;
conv_5: PointwiseConvParams;
conv_6: PointwiseConvParams;
conv_7: PointwiseConvParams;
box_predictor_0: BoxPredictionParams;
box_predictor_1: BoxPredictionParams;
box_predictor_2: BoxPredictionParams;
box_predictor_3: BoxPredictionParams;
box_predictor_4: BoxPredictionParams;
box_predictor_5: BoxPredictionParams;
};
export declare type OutputLayerParams = {
extra_dim: tf.Tensor3D;
};
export declare type NetParams = {
mobilenetv1: MobileNetV1.Params;
prediction_layer: PredictionLayerParams;
output_layer: OutputLayerParams;
};
//# sourceMappingURL=types.d.ts.map

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export {};
//# sourceMappingURL=types.js.map

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{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/ssdMobilenetv1/types.ts"],"names":[],"mappings":""}

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31
src/NeuralNetwork.ts
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@ -6,38 +6,48 @@ import { loadWeightMap } from './dom';
import { env } from './env';
export abstract class NeuralNetwork<TNetParams> {
protected _params: TNetParams | undefined = undefined
protected _paramMappings: ParamMapping[] = []
constructor(protected _name: string) {}
public get params(): TNetParams | undefined { return this._params }
public get paramMappings(): ParamMapping[] { return this._paramMappings }
public get isLoaded(): boolean { return !!this.params }
public getParamFromPath(paramPath: string): tf.Tensor {
const { obj, objProp } = this.traversePropertyPath(paramPath)
return obj[objProp]
}
public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {
const { obj, objProp } = this.traversePropertyPath(paramPath)
obj[objProp].dispose()
obj[objProp] = tensor
}
public getParamList() {
return this._paramMappings.map(({ paramPath }) => ({
path: paramPath,
tensor: this.getParamFromPath(paramPath)
}))
}
public getTrainableParams() {
return this.getParamList().filter(param => param.tensor instanceof tf.Variable)
}
public getFrozenParams() {
return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))
}
public variable() {
this.getFrozenParams().forEach(({ path, tensor }) => {
this.reassignParamFromPath(path, tensor.variable())
})
}
public freeze() {
this.getTrainableParams().forEach(({ path, tensor: variable }) => {
const tensor = tf.tensor(variable.dataSync())
@ -45,6 +55,7 @@ export abstract class NeuralNetwork<TNetParams> {
this.reassignParamFromPath(path, tensor)
})
}
public dispose(throwOnRedispose: boolean = true) {
this.getParamList().forEach(param => {
if (throwOnRedispose && param.tensor.isDisposed) {
@ -54,6 +65,7 @@ export abstract class NeuralNetwork<TNetParams> {
})
this._params = undefined
}
public serializeParams(): Float32Array {
return new Float32Array(
this.getParamList()
@ -61,64 +73,83 @@ export abstract class NeuralNetwork<TNetParams> {
.reduce((flat, arr) => flat.concat(arr))
)
}
public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {
if (weightsOrUrl instanceof Float32Array) {
this.extractWeights(weightsOrUrl)
return
}
await this.loadFromUri(weightsOrUrl)
}
public async loadFromUri(uri: string | undefined) {
if (uri && typeof uri !== 'string') {
throw new Error(`${this._name}.loadFromUri - expected model uri`)
}
const weightMap = await loadWeightMap(uri, this.getDefaultModelName())
this.loadFromWeightMap(weightMap)
}
public async loadFromDisk(filePath: string | undefined) {
if (filePath && typeof filePath !== 'string') {
throw new Error(`${this._name}.loadFromDisk - expected model file path`)
}
const { readFile } = env.getEnv()
const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())
const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(
filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))
)
const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
const manifest = JSON.parse((await readFile(manifestUri)).toString())
const weightMap = await loadWeights(manifest, modelBaseUri)
this.loadFromWeightMap(weightMap)
}
public loadFromWeightMap(weightMap: tf.NamedTensorMap) {
const {
paramMappings,
params
} = this.extractParamsFromWeigthMap(weightMap)
this._paramMappings = paramMappings
this._params = params
}
public extractWeights(weights: Float32Array) {
const {
paramMappings,
params
} = this.extractParams(weights)
this._paramMappings = paramMappings
this._params = params
}
private traversePropertyPath(paramPath: string) {
if (!this.params) {
throw new Error(`traversePropertyPath - model has no loaded params`)
}
const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {
if (!res.nextObj.hasOwnProperty(objProp)) {
throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)
}
return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }
}, { nextObj: this.params })
const { obj, objProp } = result
if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {
throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)
}
return { obj, objProp }
}

2
src/dom/loadWeightMap.ts
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@ -8,8 +8,6 @@ export async function loadWeightMap(
defaultModelName: string,
): Promise<tf.NamedTensorMap> {
const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)
const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)
return tf.io.loadWeights(manifest, modelBaseUri)
}

7
src/euclideanDistance.ts
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@ -1,12 +1,13 @@
export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {
if (arr1.length !== arr2.length) {
if (arr1.length !== arr2.length)
throw new Error('euclideanDistance: arr1.length !== arr2.length')
}
const desc1 = Array.from(arr1)
const desc2 = Array.from(arr2)
return Math.sqrt(
desc1
.map((val, i) => val - desc2[i])
.reduce((res, diff) => res + Math.pow(diff, 2), 0)
)
}
}

15
src/globalApi/DetectFacesTasks.ts
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@ -1,7 +1,9 @@
import { FaceDetection } from '../classes/FaceDetection';
import { TNetInput } from '../dom';
import { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
import { TinyYolov2Options } from '../tinyYolov2';
import { ComposableTask } from './ComposableTask';
import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
import { nets } from './nets';
@ -12,7 +14,7 @@ import { FaceDetectionOptions } from './types';
export class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {
constructor(
protected input: TNetInput,
protected options: FaceDetectionOptions = new TinyFaceDetectorOptions()
protected options: FaceDetectionOptions = new SsdMobilenetv1Options()
) {
super()
}
@ -24,10 +26,17 @@ export class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {
const { input, options } = this
const faceDetectionFunction = options instanceof TinyFaceDetectorOptions
? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)
: null
: (
options instanceof SsdMobilenetv1Options
? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)
: (
options instanceof TinyYolov2Options
? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)
: null
)
)
if (!faceDetectionFunction) {
throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')

29
src/globalApi/allFaces.ts Normal file
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@ -0,0 +1,29 @@
import { TNetInput } from '../dom';
import { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1';
import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';
import { detectAllFaces } from './detectFaces';
// export allFaces API for backward compatibility
export async function allFacesSsdMobilenetv1(
input: TNetInput,
minConfidence?: number
): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {
console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')
return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))
.withFaceLandmarks()
.withFaceDescriptors()
}
export async function allFacesTinyYolov2(
input: TNetInput,
forwardParams: ITinyYolov2Options = {}
): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {
console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')
return await detectAllFaces(input, new TinyYolov2Options(forwardParams))
.withFaceLandmarks()
.withFaceDescriptors()
}
export const allFaces = allFacesSsdMobilenetv1

7
src/globalApi/detectFaces.ts
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@ -1,19 +1,18 @@
import { TNetInput } from '../dom';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';
import { FaceDetectionOptions } from './types';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
export function detectSingleFace(
input: TNetInput,
options: FaceDetectionOptions = new TinyFaceDetectorOptions()
options: FaceDetectionOptions = new SsdMobilenetv1Options()
): DetectSingleFaceTask {
return new DetectSingleFaceTask(input, options)
}
export function detectAllFaces(
input: TNetInput,
options: FaceDetectionOptions = new TinyFaceDetectorOptions()
options: FaceDetectionOptions = new SsdMobilenetv1Options()
): DetectAllFacesTask {
return new DetectAllFacesTask(input, options)
}

1
src/globalApi/index.ts
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@ -1,3 +1,4 @@
export * from './allFaces'
export * from './ComposableTask'
export * from './ComputeFaceDescriptorsTasks'
export * from './detectFaces'

28
src/globalApi/nets.ts
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@ -11,11 +11,14 @@ import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet'
import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
import { WithFaceDetection } from '../factories/WithFaceDetection';
import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
import { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';
export const nets = {
ssdMobilenetv1: new SsdMobilenetv1(),
tinyFaceDetector: new TinyFaceDetector(),
tinyYolov2: new TinyYolov2(),
faceLandmark68Net: new FaceLandmark68Net(),
@ -25,6 +28,16 @@ export const nets = {
ageGenderNet: new AgeGenderNet()
}
/**
* Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
*
* @param input The input image.
* @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>
nets.ssdMobilenetv1.locateFaces(input, options)
/**
* Attempts to detect all faces in an image using the Tiny Face Detector.
*
@ -35,6 +48,16 @@ export const nets = {
export const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>
nets.tinyFaceDetector.locateFaces(input, options)
/**
* Attempts to detect all faces in an image using the Tiny Yolov2 Network.
*
* @param input The input image.
* @param options (optional, default: see TinyYolov2Options constructor for default parameters).
* @returns Bounding box of each face with score.
*/
export const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>
nets.tinyYolov2.locateFaces(input, options)
/**
* Detects the 68 point face landmark positions of the face shown in an image.
*
@ -91,6 +114,7 @@ export const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressi
export const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>
nets.ageGenderNet.predictAgeAndGender(input)
export const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)
export const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)
export const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)
export const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)
@ -100,6 +124,6 @@ export const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.l
export const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)
// backward compatibility
export const loadFaceDetectionModel = loadTinyFaceDetectorModel
export const locateFaces = TinyFaceDetector
export const loadFaceDetectionModel = loadSsdMobilenetv1Model
export const locateFaces = ssdMobilenetv1
export const detectLandmarks = detectFaceLandmarks

4
src/globalApi/types.ts
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@ -1,7 +1,9 @@
import { FaceDetection } from '../classes/FaceDetection';
import { TNetInput } from '../dom';
import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
import { TinyYolov2Options } from '../tinyYolov2';
export type FaceDetectionOptions = TinyFaceDetectorOptions
export type FaceDetectionOptions = TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options
export type FaceDetectionFunction = (input: TNetInput) => Promise<FaceDetection[]>

13
src/index.ts
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@ -1,8 +1,13 @@
import * as tf from '@tensorflow/tfjs';
import * as tf from '@tensorflow/tfjs-core';
import * as draw from './draw';
import * as utils from './utils';
export { draw, utils, tf }
export {
draw,
utils,
tf
}
export * from './ageGenderNet/index';
export * from './classes/index';
@ -14,8 +19,10 @@ export * from './faceRecognitionNet/index';
export * from './factories/index';
export * from './globalApi/index';
export * from './ops/index';
export * from './ssdMobilenetv1/index';
export * from './tinyFaceDetector/index';
export * from './tinyYolov2/index';
export * from './euclideanDistance';
export * from './NeuralNetwork';
export * from './resizeResults';
export * from './resizeResults';

8
src/resizeResults.ts
View File

@ -5,23 +5,31 @@ import { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFa
import { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';
export function resizeResults<T>(results: T, dimensions: IDimensions): T {
const { width, height } = new Dimensions(dimensions.width, dimensions.height)
if (width <= 0 || height <= 0) {
throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)
}
if (Array.isArray(results)) {
return results.map(obj => resizeResults(obj, { width, height })) as any as T
}
if (isWithFaceLandmarks(results)) {
const resizedDetection = results.detection.forSize(width, height)
const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)
return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)
}
if (isWithFaceDetection(results)) {
return extendWithFaceDetection(results, results.detection.forSize(width, height))
}
if (results instanceof FaceLandmarks || results instanceof FaceDetection) {
return (results as any).forSize(width, height)
}
return results
}

133
src/ssdMobilenetv1/SsdMobilenetv1.ts Normal file
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@ -0,0 +1,133 @@
import * as tf from '@tensorflow/tfjs-core';
import { Rect } from '../classes';
import { FaceDetection } from '../classes/FaceDetection';
import { NetInput, TNetInput, toNetInput } from '../dom';
import { NeuralNetwork } from '../NeuralNetwork';
import { extractParams } from './extractParams';
import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';
import { NetParams } from './types';
export class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
constructor() {
super('SsdMobilenetv1')
}
public forwardInput(input: NetInput) {
const { params } = this
if (!params) {
throw new Error('SsdMobilenetv1 - load model before inference')
}
return tf.tidy(() => {
const batchTensor = input.toBatchTensor(512, false).toFloat()
const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D
const features = mobileNetV1(x, params.mobilenetv1)
const {
boxPredictions,
classPredictions
} = predictionLayer(features.out, features.conv11, params.prediction_layer)
return outputLayer(boxPredictions, classPredictions, params.output_layer)
})
}
public async forward(input: TNetInput) {
return this.forwardInput(await toNetInput(input))
}
public async locateFaces(
input: TNetInput,
options: ISsdMobilenetv1Options = {}
): Promise<FaceDetection[]> {
const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)
const netInput = await toNetInput(input)
const {
boxes: _boxes,
scores: _scores
} = this.forwardInput(netInput)
// TODO batches
const boxes = _boxes[0]
const scores = _scores[0]
for (let i = 1; i < _boxes.length; i++) {
_boxes[i].dispose()
_scores[i].dispose()
}
// TODO find a better way to filter by minConfidence
const scoresData = Array.from(await scores.data())
const iouThreshold = 0.5
const indices = nonMaxSuppression(
boxes,
scoresData,
maxResults,
iouThreshold,
minConfidence
)
const reshapedDims = netInput.getReshapedInputDimensions(0)
const inputSize = netInput.inputSize as number
const padX = inputSize / reshapedDims.width
const padY = inputSize / reshapedDims.height
const boxesData = boxes.arraySync()
const results = indices
.map(idx => {
const [top, bottom] = [
Math.max(0, boxesData[idx][0]),
Math.min(1.0, boxesData[idx][2])
].map(val => val * padY)
const [left, right] = [
Math.max(0, boxesData[idx][1]),
Math.min(1.0, boxesData[idx][3])
].map(val => val * padX)
return new FaceDetection(
scoresData[idx],
new Rect(
left,
top,
right - left,
bottom - top
),
{
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
}
)
})
boxes.dispose()
scores.dispose()
return results
}
protected getDefaultModelName(): string {
return 'ssd_mobilenetv1_model'
}
protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
return extractParamsFromWeigthMap(weightMap)
}
protected extractParams(weights: Float32Array) {
return extractParams(weights)
}
}

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