Function toNetInput

src/dom/toNetInput.ts:15–57

(inputs: TNetInput)

 * @returns A NetInput instance, which can be passed into one of the neural networks.
 */
export async function toNetInput(inputs: TNetInput): Promise<NetInput> {
  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))
}