import numpy as np
from typing import Optional, Tuple
import cv2
from classes.parameters import Parameters  # Ensure correct import path is set
from classes.position_estimator import PositionEstimator  # Ensure correct import path is set
from classes.trackers.base_tracker import BaseTracker  # Ensure correct import path is set

class CustomTracker(BaseTracker):
    """
    Template for implementing a custom object tracking algorithm extending the BaseTracker class.
    
    This template serves as a guide for developers to implement their own tracking algorithms,
    providing the necessary steps and methods to be overridden or utilized, ensuring compatibility
    and functionality within the existing tracking framework.
    """
    
    def __init__(self, video_handler: Optional[object] = None, detector: Optional[object] = None,app_controller: Optional[object] = None):
        """
        Initializes the custom tracker with an optional video handler and detector.
        
        Parameters:
        - video_handler: Handler for video streaming and processing. This could be an object managing video input.
        - detector: Object detector for initializing tracking. This could be a pre-trained model for detecting objects.
        
        Note: Ensure to initialize any tracker-specific resources or parameters here.
        """
        super().__init__(video_handler, detector)
        # Initialize tracker-specific resources or parameters
        # Example: self.custom_tracker_resource = SomeResource()
        
    def start_tracking(self, frame: np.ndarray, bbox: Tuple[int, int, int, int]) -> None:
        """
        Starts the tracking process with the given frame and bounding box.
        
        This method should initialize the tracking algorithm with the provided bounding box on the frame.
        
        Parameters:
        - frame: The initial video frame to start tracking.
        - bbox: A tuple representing the bounding box (x, y, width, height) for initializing tracking.
        
        Note: Implement the initialization logic for your tracking algorithm here.
        """
        # Example initialization logic
        # self.tracker_specific_method.initialize(frame, bbox)
        pass

    def update(self, frame: np.ndarray) -> Tuple[bool, Tuple[int, int, int, int]]:
        """
        Updates the tracker with the current frame and returns the tracking success status and the new bounding box.
        
        This method should apply the tracking algorithm on the current frame and return whether tracking was
        successful along with the updated bounding box if tracking was successful.
        
        Parameters:
        - frame: The current video frame to be processed by the tracking algorithm.
        
        Returns:
        - A tuple containing the tracking success status and the updated bounding box (x, y, width, height).
        
        Note: Implement the update logic for your tracking algorithm here, updating the bounding box and tracking status.
        """
        # Example update logic
        # success, updated_bbox = self.tracker_specific_method.update(frame)
        # return success, updated_bbox
        pass

    # Optional: Override or implement additional methods specific to your tracking algorithm.
    # For example, handling tracker reinitialization, providing debug information, etc.
