import { TrainingParameters as HandTrainingParams } from './gtm-hand/teachable-handpose';
import { TeachableHandPose, type Metadata as HandMetadata } from './gtm-hand';
import * as tf from '@tensorflow/tfjs';
import type { TeachableModel, ExplainedPredictionsOutput, TMType } from './TeachableModel';
import { AudioExample } from './gtm-utils/recorder';
interface TrainingParameters extends HandTrainingParams {
}
interface BaseMetadata {
    modelBaseUrl?: string;
}
export declare type Metadata = BaseMetadata & HandMetadata;
export default class HandModel implements TeachableModel {
    protected model?: TeachableHandPose;
    protected _ready?: Promise<boolean>;
    protected trained: boolean;
    protected busy: boolean;
    protected imageSize: number;
    protected _disposed: boolean;
    variant: TMType;
    explained?: HTMLCanvasElement;
    modelBaseUrl: string;
    private lastHands;
    constructor(type: TMType, metadata?: Metadata, model?: tf.io.ModelJSON, weights?: ArrayBuffer);
    getVariant(): TMType;
    setXAICanvas(canvas: HTMLCanvasElement): void;
    setXAIClass(): void;
    protected load(metadata?: HandMetadata, model?: tf.io.ModelJSON, weights?: ArrayBuffer): Promise<void>;
    ready(): Promise<boolean>;
    draw(image: HTMLCanvasElement): HTMLCanvasElement;
    estimate(image: HTMLCanvasElement): Promise<HTMLCanvasElement>;
    predict(image: HTMLCanvasElement, staticImageMode?: boolean): Promise<ExplainedPredictionsOutput>;
    train(params: TrainingParameters, callbacks: tf.CustomCallbackArgs): Promise<tf.LayersModel>;
    addExample(className: number, image: HTMLCanvasElement | AudioExample, staticImageMode?: boolean): Promise<void>;
    dispose(): void;
    setName(name: string): void;
    getModel(): TeachableHandPose | undefined;
    getImageSize(): number;
    isTrained(): boolean;
    isReady(): boolean;
    setSeed(seed: string): void;
    getMetadata(): HandMetadata;
    save(handler: tf.io.IOHandler): Promise<tf.io.SaveResult>;
    setLabels(labels: string[]): void;
    getLabels(): string[];
    getLabel(ix: number): string;
    getNumExamples(): number;
    getExamplesPerClass(): number[];
    getNumValidation(): number;
    calculateAccuracy(): Promise<{
        reference: tf.Tensor<tf.Rank>;
        predictions: tf.Tensor<tf.Rank>;
    }>;
}
export {};