import supervision as sv
import football_analytics.config as config

class FootballTracker:
    def __init__(self, detector, tracker_config=None):
        """
        Initializes the tracker using the YOLO model from the detector.
        
        Args:
            detector (FootballDetector): An instance of FootballDetector
            tracker_config (str): Path or name of the tracker config (e.g. 'botsort.yaml')
        """
        self.detector = detector
        self.tracker_config = tracker_config or config.TRACKER_CONFIG
        print(f"[Tracker] Initialized with config: {self.tracker_config}")

    def track(self, frame):
        """
        Tracks players and the ball across frames.
        
        Args:
            frame (np.ndarray): Video frame (BGR format from OpenCV)
            
        Returns:
            sv.Detections: Supervision Detections object containing tracker_id.
        """
        # Run YOLO in tracking mode
        results = self.detector.model.track(
            frame,
            persist=True,
            conf=self.detector.conf_threshold,
            iou=self.detector.iou_threshold,
            tracker=self.tracker_config,
            classes=self.detector.allowed_classes,
            verbose=False,
            device=self.detector.device
        )[0]
        
        # Convert to supervision detections (automatically parses tracker_id from boxes.id)
        detections = sv.Detections.from_ultralytics(results)
        
        # Sometimes class_id is empty or None, ensure arrays are initialized properly
        if detections.tracker_id is None and len(detections) > 0:
            # Fallback if tracker didn't assign IDs (e.g., first frame or tracking failed)
            # We assign a temporary negative ID or None
            import numpy as np
            detections.tracker_id = np.array([-1] * len(detections))
            
        return detections