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justadudewhohacksjustadudewhohacks
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use async toNetInput instead creating NetInput directly, which awaits all media elements of the input to be finished loading
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‎build/NetInput.d.ts

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import { Dimensions, TNetInput } from './types';
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import { Dimensions, TMediaElement } from './types';
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export declare class NetInput {
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private _canvases;
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constructor(mediaArg: TNetInput, dims?: Dimensions);
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constructor(medias: Array<TMediaElement>, dims?: Dimensions);
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private initCanvas(media, dims?);
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readonly canvases: HTMLCanvasElement[];
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readonly width: number;

‎build/NetInput.js

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‎build/NetInput.js.map

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‎build/allFacesFactory.js

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‎build/allFacesFactory.js.map

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‎build/commons/getImageTensor.d.ts

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import * as tf from '@tensorflow/tfjs-core';
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import { NetInput } from '../NetInput';
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export declare function getImageTensor(input: tf.Tensor | NetInput): tf.Tensor4D;

‎build/commons/getImageTensor.js

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‎build/commons/getImageTensor.js.map

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‎build/extractFaceTensors.d.ts

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* @param detections The face detection results or face bounding boxes for that image.
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* @returns Tensors of the corresponding image region for each detected face.
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*/
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export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: Array<FaceDetection | Rect>): tf.Tensor4D[];
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export declare function extractFaceTensors(input: tf.Tensor | NetInput | TNetInput, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor4D[]>;

‎build/extractFaceTensors.js

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