import { DetectionResults } from "./types/Detector";
declare global {
    interface Window {
        cardDetector?: CardDetector;
    }
}
declare class CardDetector {
    static IMG_SIZE: number;
    private model;
    private customModelPath;
    private model_name;
    private cdnPath;
    isFlippedSession: boolean;
    private statusCallback;
    private _modelState;
    private laplaceFilter;
    /** 5x5 Gaussian-like blur kernel for RGB images used in blur detection */
    private blurKernel;
    private embedModel;
    private referenceEmbedding;
    constructor(modelPath?: string);
    setModelStatusCallback(callback: (state: string) => void): void;
    private setModelState;
    get modelState(): string;
    private load_model;
    private detectDistanceProblems;
    private warm_model;
    private tf_mem_log;
    private setupEmbeddingModel;
    private extractEmbedding;
    private setReferenceCard;
    private calculateCardSimilarity;
    private processCardSimilarity;
    cleanupEmbeddings(): void;
    disposeAll(): void;
    /**
     * Detects image blur by comparing embeddings of original vs artificially blurred image.
     *
     * @param imgTensor - Input image tensor [1, 224, 224, 3]
     * @returns Blur detection results or null if embeddings not available
     *
     * @description
     * - Blur score: 0.0 (very sharp) to 1.0 (very blurry)
     * - Sharp images change significantly when blurred → low score
     * - Blurry images barely change when blurred more → high score
     * - Current threshold: < 0.90 = sharp, ≥ 0.90 = blurry
     */
    private detectBlur;
    private prepare_results;
    predict_image(canvas: HTMLCanvasElement, flipHorizontal: boolean, flipVertical: boolean): Promise<DetectionResults | null>;
}
declare class DetectorWrapper {
    static image_size: number;
    static get(): CardDetector;
    static init(modelPath?: string): void;
    static cleanUp(): void;
}
export declare class CardScanModel {
    static warm(modelPath?: string): void;
}
export default DetectorWrapper;