ppu-yolo-onnx-inference/core/base-yolo-inference.d.ts

Use your YOLO onnx object detection model in Typescript Bun environment easily.

import type { DebuggingOptions, DetectedObject, ModelThresholds, YoloDetectionOptions } from "../interface.js";
import type { CoreCanvas, PlatformProvider } from "./platform.js";
/**
 * Platform-agnostic YOLOv11 Object Detection Inference Engine.
 *
 * Contains all business logic for image preprocessing, model inference,
 * and NMS post-processing. Platform-specific behaviour (canvas creation,
 * ONNX runtime, file I/O) is delegated to the injected {@link PlatformProvider}.
 */
export declare class BaseYoloDetectionInference {
    private readonly model;
    /** Class names for object detection labels. */
    protected readonly classNames: string[];
    /** Merged threshold configuration. */
    protected readonly thresholds: Required<ModelThresholds>;
    /** Merged debugging configuration. */
    protected readonly debugging: Required<DebuggingOptions>;
    /** Platform abstraction layer. */
    protected readonly platform: PlatformProvider;
    private modelMetadata;
    private session;
    private static readonly CHANNELS;
    constructor(options: YoloDetectionOptions, platform: PlatformProvider);
    /**
     * Whether the model session has been initialised.
     * @returns `true` after a successful {@link init} call.
     */
    isInitialized(): boolean;
    /**
     * Initialize the YOLO model and prepare for inference.
     */
    init(): Promise<void>;
    /**
     * Detect objects in an image.
     * @param image - The input image as ArrayBuffer or platform canvas.
     * @returns An array of detected objects with bounding boxes, class names, and confidence scores.
     * @throws Error if the model is not initialized or detection fails.
     */
    detect(image: ArrayBuffer | CoreCanvas): Promise<DetectedObject[]>;
    private preprocessImage;
    private canvasToTensor;
    private runInference;
    private postprocessOutput;
    private extractCandidates;
    private extractWithLowerThreshold;
    private debugTensorData;
    private applyNMS;
    private scaleCandidates;
    private calculateIoU;
    private saveDebugImages;
    private savePreprocessedImage;
    private saveDetectionVisualization;
    /** Log a message when verbose debugging is enabled. */
    protected log(caller: string, message: string): void;
    /**
     * Releases the ONNX runtime session and cleans up resources.
     * Safe to call even if the model was never initialised.
     */
    destroy(): Promise<void>;
}