import CVModel, { RFObjectDetectionPrediction } from "./CVModel";
import * as tf from "@tensorflow/tfjs";
import CVImage from "../utils/image";
import { ModelJSON } from "@tensorflow/tfjs-core/dist/io/types";
export declare class YOLOOptions {
    model?: ModelJSON | null;
    size?: number;
    maxNumBoxes?: number;
    scoreThreshold?: number;
    iouThreshold?: number;
    classes?: string[];
    colors?: {
        [key: string]: string;
    };
}
declare class DefaultYOLOOptions {
    model: ModelJSON | null;
    size: number;
    maxNumBoxes: number;
    scoreThreshold: number;
    iouThreshold: number;
    classes: string[];
    colors: {
        [key: string]: string;
    };
    constructor(options: YOLOOptions);
    update(options: YOLOOptions): void;
}
export default class YOLOv8 extends CVModel<RFObjectDetectionPrediction[]> {
    options: DefaultYOLOOptions;
    model?: tf.GraphModel;
    async_model: boolean;
    constructor(options: YOLOOptions);
    initialize(): Promise<void>;
    configure(options: YOLOOptions): void;
    zeros(): tf.Tensor<tf.Rank>;
    preprocess(image: CVImage): Promise<tf.Tensor<tf.Rank>>;
    infer(image: CVImage): Promise<RFObjectDetectionPrediction[]>;
    buildDetectedObjects(width: number, height: number, boxes: Float32Array, scores: number[], indexes: Float32Array, classes: number[]): RFObjectDetectionPrediction[];
    calculateMaxScoresYolov5(scores: Float32Array, numBoxes: number, numClasses: number): any;
    getBoxesYoloV5(scores: Float32Array, numBoxes: number, numClasses: number, scale_by: number): Float32Array;
}
export {};