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 RFDETROptions {
    model?: ModelJSON | null;
    size?: number;
    maxNumBoxes?: number;
    scoreThreshold?: number;
    classes?: string[];
    colors?: {
        [key: string]: string;
    };
}
declare class DefaultRFDETROptions {
    model: ModelJSON | null;
    size: number;
    maxNumBoxes: number;
    scoreThreshold: number;
    classes: string[];
    colors: {
        [key: string]: string;
    };
    constructor(options: RFDETROptions);
    update(options: RFDETROptions): void;
}
export default class RFDetr extends CVModel<RFObjectDetectionPrediction[]> {
    options: DefaultRFDETROptions;
    model?: tf.GraphModel;
    async_model: boolean;
    imagenetMeans: tf.Tensor;
    imagenetStds: tf.Tensor;
    constructor(options: RFDETROptions);
    initialize(): Promise<void>;
    configure(options: RFDETROptions): void;
    zeros(): tf.Tensor<tf.Rank>;
    preprocess(image: CVImage): Promise<tf.Tensor<tf.Rank>>;
    infer(image: CVImage): Promise<{
        bbox: {
            x: number;
            y: number;
            width: number;
            height: number;
        };
        class: string;
        confidence: number;
        color: string;
    }[]>;
    processDetections(boxesData: Float32Array, scoresData: Float32Array, boxesShape: number[], scoresShape: number[], confidenceThreshold: number, imageWidth: number, imageHeight: number): {
        bbox: {
            x: number;
            y: number;
            width: number;
            height: number;
        };
        class: string;
        confidence: number;
        color: string;
    }[];
}
export {};