@the-horizon-dev/fast-face-detection/dist/services/logger-service.d.ts

Fast face detection package using TensorFlow.js MediaPipe models for browser and Node.js environments

/**
 * @file Logger service for consistent logging throughout the library
 *
 * This service provides a centralized logging mechanism with support for
 * different log levels and formatted output. It helps with debugging and
 * troubleshooting by providing consistent log formatting and control over
 * verbosity.
 */
/**
 * Service responsible for consistent logging across the face detection library.
 * All library logs are channeled through this service to ensure uniform formatting
 * and to allow global control over log verbosity.
 *
 * @example
 * // Basic usage
 * Logger.info('Processing started');
 * Logger.error('Failed to load model', new Error('Network error'));
 *
 * // Enable debug logs
 * Logger.setDebug(true);
 * Logger.debug('Detailed processing information');
 */
export declare class Logger {
    /** Flag to control debug logging visibility */
    private static isDebugEnabled;
    /**
     * Enables or disables debug mode for detailed logging.
     * When debug mode is disabled, debug and performance logs won't be shown.
     *
     * @param {boolean} enabled - Whether to enable debug logging
     *
     * @example
     * // Enable debug logs during development
     * Logger.setDebug(true);
     *
     * // Disable debug logs in production
     * Logger.setDebug(false);
     */
    static setDebug(enabled: boolean): void;
    /**
     * Logs an error message and optional error object.
     * Use this for critical errors that prevent normal operation.
     *
     * @param {string} message - The error message to display
     * @param {Error} [error] - Optional error object with stack trace
     *
     * @example
     * try {
     *   // Some operation that might fail
     * } catch (error) {
     *   Logger.error('Failed to process image', error as Error);
     * }
     */
    static error(message: string, error?: Error): void;
    /**
     * Logs a warning message and optional error object.
     * Use this for non-critical issues that allow continued operation.
     *
     * @param {string} message - The warning message to display
     * @param {Error} [error] - Optional error object with details
     *
     * @example
     * if (!isOptimalFormat) {
     *   Logger.warn('Image format not optimal, processing may be slower');
     * }
     */
    static warn(message: string, error?: Error): void;
    /**
     * Logs an informational message.
     * Use this for general status updates and important events.
     *
     * @param {string} message - The information message to display
     *
     * @example
     * Logger.info('Face detection model loaded successfully');
     * Logger.info(`Detected ${faces.length} faces in the image`);
     */
    static info(message: string): void;
    /**
     * Logs a debug message with optional additional arguments.
     * Debug messages are only shown when debug mode is enabled.
     *
     * @param {string} message - The debug message to display
     * @param {...unknown[]} args - Optional additional arguments to log
     *
     * @example
     * // Log a simple debug message
     * Logger.debug('Processing step 1');
     *
     * // Log with additional data
     * Logger.debug('Face detected', { x: 100, y: 200 });
     */
    static debug(message: string, ...args: unknown[]): void;
    /**
     * Logs performance metrics for operations.
     * These messages only appear if debug mode is enabled.
     * Use this to track execution time of performance-critical operations.
     *
     * @param {string} operation - Name of the operation being measured
     * @param {number} durationMs - Duration in milliseconds
     *
     * @example
     * const startTime = performance.now();
     * // ... perform face detection ...
     * const endTime = performance.now();
     * Logger.performance('Face detection', endTime - startTime);
     */
    static performance(operation: string, durationMs: number): void;
}