import type { InferenceSession } from "onnxruntime-common";
import type { DocLayoutOptions, DocLayoutResult, DocLayoutResultWithMasks } from "../interface.js";
import type { CoreCanvas, PlatformProvider } from "./platform.js";
/**
 * Abstract base class for platform-agnostic document layout analysis.
 *
 * Concrete implementations (`DocLayoutService` for Node/Web) extend this
 * class and provide a {@link PlatformProvider}.
 */
export declare abstract class BaseDocLayoutService {
    protected options: DocLayoutOptions;
    protected session: InferenceSession | null;
    protected readonly platform: PlatformProvider;
    constructor(platform: PlatformProvider, options?: DocLayoutOptions);
    protected log(message: string): void;
    protected abstract initSessions(): Promise<void>;
    /**
     * Run layout analysis on an image.
     *
     * The model outputs regions in reading order, preserving the document's
     * natural reading structure.
     *
     * @param image - The source image as an `ArrayBuffer` or platform canvas.
     * @returns Layout analysis result with detected regions in reading order.
     */
    analyze(image: ArrayBuffer | CoreCanvas): Promise<DocLayoutResult | DocLayoutResultWithMasks>;
    /** Prepare a canvas from an ArrayBuffer image. */
    private prepareCanvas;
    /**
     * Resize, normalize, and convert image to CHW float32 tensor.
     */
    private preprocess;
    /**
     * Parse raw model output into LayoutBox array sorted by reading order.
     *
     * V2 boxes: [cls_id, score, x1, y1, x2, y2, reading_order, ?]
     * V3 boxes: [cls_id, score, x1, y1, x2, y2, reading_order]
     *
     * Column 6 contains the model's baked-in reading order index.
     * After sorting, cross-class NMS removes overlapping duplicate detections.
     */
    private postprocess;
    /**
     * Merge same-label contained boxes.
     *
     * When two boxes share the same label and one is largely contained inside
     * the other (IoMin > 0.6), the inner box is dropped since the outer box
     * already covers that content.
     */
    private mergeSameLabelContained;
    /**
     * Cross-class Non-Maximum Suppression using IoU.
     *
     * Suppresses near-duplicate boxes (same spatial region, different labels).
     * A high IoU threshold (0.8) ensures only true duplicates are removed.
     */
    private applyCrossClassNMS;
    /** Intersection over Union between two [x1, y1, x2, y2] boxes. */
    private computeIoU;
    /** Intersection over Minimum area — detects containment. */
    private computeIoMin;
    /**
     * Extract per-region masks from V3 output, filtered by threshold.
     */
    private extractMasks;
    /**
     * Draw annotated bounding boxes on the original image and save to disk.
     */
    private debugAnnotatedImage;
}