import { isTensor3D, isTensor4D } from '../utils'; import { awaitMediaLoaded } from './awaitMediaLoaded'; import { isMediaElement } from './isMediaElement'; import { NetInput } from './NetInput'; import { resolveInput } from './resolveInput'; import { TNetInput } from './types'; /** * Validates the input to make sure, they are valid net inputs and awaits all media elements * to be finished loading. * * @param input The input, which can be a media element or an array of different media elements. * @returns A NetInput instance, which can be passed into one of the neural networks. */ export async function toNetInput(inputs: TNetInput): Promise { if (inputs instanceof NetInput) { return inputs } let inputArgArray = Array.isArray(inputs) ? inputs : [inputs] if (!inputArgArray.length) { throw new Error('toNetInput - empty array passed as input') } const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : '' const inputArray = inputArgArray.map(resolveInput) inputArray.forEach((input, i) => { if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) { if (typeof inputArgArray[i] === 'string') { throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`) } throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`) } if (isTensor4D(input)) { // if tf.Tensor4D is passed in the input array, the batch size has to be 1 const batchSize = input.shape[0] if (batchSize !== 1) { throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`) } } }) // wait for all media elements being loaded await Promise.all( inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input)) ) return new NetInput(inputArray, Array.isArray(inputs)) }