import * as tf from '@tensorflow/tfjs-core'; import { Dimensions } from '../classes/Dimensions'; import { TResolvedNetInput } from './types'; export declare class NetInput { private _imageTensors; private _canvases; private _batchSize; private _treatAsBatchInput; private _inputDimensions; private _inputSize; constructor(inputs: Array<TResolvedNetInput>, treatAsBatchInput?: boolean); get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D>; get canvases(): HTMLCanvasElement[]; get isBatchInput(): boolean; get batchSize(): number; get inputDimensions(): number[][]; get inputSize(): number | undefined; get reshapedInputDimensions(): Dimensions[]; getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement; getInputDimensions(batchIdx: number): number[]; getInputHeight(batchIdx: number): number; getInputWidth(batchIdx: number): number; getReshapedInputDimensions(batchIdx: number): Dimensions; /** * Create a batch tensor from all input canvases and tensors * with size [batchSize, inputSize, inputSize, 3]. * * @param inputSize Height and width of the tensor. * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on * both sides of the minor dimension oof the image. * @returns The batch tensor. */ toBatchTensor(inputSize: number, isCenterInputs?: boolean): tf.Tensor4D; }