from dataclasses import asdict
import time
from datetime import datetime, timezone
import copy  # Added for deep copying detections to preserve original masks
from typing import Any, Dict, List, Optional
from ..core.base import (
    BaseProcessor,
    ProcessingContext,
    ProcessingResult,
    ConfigProtocol,
    ResultFormat,
)
from ..utils import (
    filter_by_confidence,
    filter_by_categories,
    apply_category_mapping,
    count_objects_by_category,
    count_objects_in_zones,
    calculate_counting_summary,
    match_results_structure,
    bbox_smoothing,
    BBoxSmoothingConfig,
    BBoxSmoothingTracker,
)
from dataclasses import dataclass, field
from ..core.config import BaseConfig, AlertConfig, ZoneConfig


@dataclass
class LicensePlateConfig(BaseConfig):
    """Configuration for License plate detection use case in License plate monitoring."""

    # Smoothing configuration
    enable_smoothing: bool = True
    smoothing_algorithm: str = "observability"  # "window" or "observability"
    smoothing_window_size: int = 20
    smoothing_cooldown_frames: int = 5
    smoothing_confidence_range_factor: float = 0.5

    # confidence thresholds
    confidence_threshold: float = 0.6

    usecase_categories: List[str] = field(default_factory=lambda: ["license_plate"])

    target_categories: List[str] = field(default_factory=lambda: ["license_plate"])

    alert_config: Optional[AlertConfig] = None

    index_to_category: Optional[Dict[int, str]] = field(
        default_factory=lambda: {
            0: "license_plate",
        }
    )


class LicensePlateUseCase(BaseProcessor):

    # Human-friendly display names for  categories
    CATEGORY_DISPLAY = {
        "license_plate": "license_plate",
    }

    def __init__(self):
        super().__init__("license_plate_detection")
        self.category = "license_plate"

        # List of  categories to track
        self.target_categories = ["license_plate"]

        self.CASE_TYPE: Optional[str] = "license_plate_detection"
        self.CASE_VERSION: Optional[str] = "1.3"

        # Initialize smoothing tracker
        self.smoothing_tracker = None

        # Initialize advanced tracker (will be created on first use)
        self.tracker = None

        # Initialize tracking state variables
        self._total_frame_counter = 0
        self._global_frame_offset = 0

        # Track start time for "TOTAL SINCE" calculation
        self._tracking_start_time = None

        # ------------------------------------------------------------------ #
        # Canonical tracking aliasing to avoid duplicate counts              #
        # ------------------------------------------------------------------ #
        # Maps raw tracker-generated IDs to stable canonical IDs that persist
        # even if the underlying tracker re-assigns a new ID after a short
        # interruption. This mirrors the logic used in people_counting to
        # provide accurate unique counting.
        self._track_aliases: Dict[Any, Any] = {}
        self._canonical_tracks: Dict[Any, Dict[str, Any]] = {}
        # Tunable parameters – adjust if necessary for specific scenarios
        self._track_merge_iou_threshold: float = 0.05  # IoU ≥ 0.05 →
        self._track_merge_time_window: float = 7.0  # seconds within which to merge

        self._ascending_alert_list: List[int] = []
        self.current_incident_end_timestamp: str = "N/A"

    def process(
        self,
        data: Any,
        config: ConfigProtocol,
        context: Optional[ProcessingContext] = None,
        stream_info: Optional[Dict[str, Any]] = None,
    ) -> ProcessingResult:
        """
        Main entry point for post-processing.
        Applies category mapping, smoothing, counting, alerting, and summary generation.
        Returns a ProcessingResult with all relevant outputs.
        """
        start_time = time.time()
        # Ensure config is correct type
        if not isinstance(config, LicensePlateConfig):
            return self.create_error_result(
                "Invalid config type",
                usecase=self.name,
                category=self.category,
                context=context,
            )
        if context is None:
            context = ProcessingContext()

        # Detect input format and store in context
        input_format = match_results_structure(data)
        context.input_format = input_format
        context.confidence_threshold = config.confidence_threshold

        # Step 1: Confidence filtering
        if config.confidence_threshold is not None:
            processed_data = filter_by_confidence(data, config.confidence_threshold)
        else:
            processed_data = data
            self.logger.debug(
                f"Did not apply confidence filtering with threshold since nothing was provided"
            )

        # Step 2: Apply category mapping if provided
        if config.index_to_category:
            processed_data = apply_category_mapping(
                processed_data, config.index_to_category
            )

        # Step 3: Category filtering
        if config.target_categories:
            processed_data = [
                d for d in processed_data if d.get("category") in self.target_categories
            ]

        # Step 4: Apply bbox smoothing if enabled
        # Deep-copy detections so that we preserve the original masks before any
        # smoothing/tracking logic potentially removes them.
        raw_processed_data = [copy.deepcopy(det) for det in processed_data]
        if config.enable_smoothing:
            if self.smoothing_tracker is None:
                smoothing_config = BBoxSmoothingConfig(
                    smoothing_algorithm=config.smoothing_algorithm,
                    window_size=config.smoothing_window_size,
                    cooldown_frames=config.smoothing_cooldown_frames,
                    confidence_threshold=config.confidence_threshold,
                    confidence_range_factor=config.smoothing_confidence_range_factor,
                    enable_smoothing=True,
                )
                self.smoothing_tracker = BBoxSmoothingTracker(smoothing_config)

            processed_data = bbox_smoothing(
                processed_data, self.smoothing_tracker.config, self.smoothing_tracker
            )
            # Restore masks after smoothing

        # Step 5: Advanced tracking (BYTETracker-like)
        try:
            from ..advanced_tracker import AdvancedTracker
            from ..advanced_tracker.config import TrackerConfig

            if self.tracker is None:
                # Configure tracker thresholds based on the use-case confidence threshold so that
                # low-confidence detections (e.g. < 0.7) can still be initialised as tracks when
                # the user passes a lower `confidence_threshold` in the post-processing config.
                if config.confidence_threshold is not None:
                    tracker_config = TrackerConfig(
                        track_high_thresh=float(config.confidence_threshold),
                        # Allow even lower detections to participate in secondary association
                        track_low_thresh=max(
                            0.05, float(config.confidence_threshold) / 2
                        ),
                        new_track_thresh=float(config.confidence_threshold),
                    )
                else:
                    tracker_config = TrackerConfig()
                self.tracker = AdvancedTracker(tracker_config)
                self.logger.info(
                    "Initialized AdvancedTracker for Monitoring and tracking with thresholds: "
                    f"high={tracker_config.track_high_thresh}, "
                    f"low={tracker_config.track_low_thresh}, "
                    f"new={tracker_config.new_track_thresh}"
                )

            # The tracker expects the data in the same format as input
            # It will add track_id and frame_id to each detection
            processed_data = self.tracker.update(processed_data)
        except Exception as e:
            # If advanced tracker fails, fallback to unsmoothed detections
            self.logger.warning(f"AdvancedTracker failed: {e}")

        # Update tracking state for total count per label
        self._update_tracking_state(processed_data)

        # ------------------------------------------------------------------ #
        # Re-attach segmentation masks that were present in the original input
        # but may have been stripped during smoothing/tracking. We match each
        # processed detection back to the raw detection with the highest IoU
        # and copy over its "masks" field (if available).
        # ------------------------------------------------------------------ #
        processed_data = self._attach_masks_to_detections(
            processed_data, raw_processed_data
        )

        # Update frame counter
        self._total_frame_counter += 1

        # Extract frame information from stream_info
        frame_number = None
        if stream_info:
            input_settings = stream_info.get("input_settings", {})
            start_frame = input_settings.get("start_frame")
            end_frame = input_settings.get("end_frame")
            # If start and end frame are the same, it's a single frame
            if (
                start_frame is not None
                and end_frame is not None
                and start_frame == end_frame
            ):
                frame_number = start_frame

        # Compute summaries and alerts
        general_counting_summary = calculate_counting_summary(data)
        counting_summary = self._count_categories(processed_data, config)
        # Add total unique counts after tracking using only local state
        total_counts = self.get_total_counts()
        counting_summary["total_counts"] = total_counts

        alerts = self._check_alerts(counting_summary, frame_number, config)
        predictions = self._extract_predictions(processed_data)

        # Step: Generate structured events and tracking stats with frame-based keys
        incidents_list = self._generate_incidents(
            counting_summary, alerts, config, frame_number, stream_info
        )
        tracking_stats_list = self._generate_tracking_stats(
            counting_summary, alerts, config, frame_number, stream_info
        )
        # business_analytics_list = self._generate_business_analytics(counting_summary, alerts, config, frame_number, stream_info, is_empty=False)
        business_analytics_list = []
        summary_list = self._generate_summary(
            counting_summary,
            incidents_list,
            tracking_stats_list,
            business_analytics_list,
            alerts,
        )

        # Extract frame-based dictionaries from the lists
        incidents = incidents_list[0] if incidents_list else {}
        tracking_stats = tracking_stats_list[0] if tracking_stats_list else {}
        business_analytics = (
            business_analytics_list[0] if business_analytics_list else {}
        )
        summary = summary_list[0] if summary_list else {}
        agg_summary = {
            str(frame_number): {
                "incidents": incidents,
                "tracking_stats": tracking_stats,
                "business_analytics": business_analytics,
                "alerts": alerts,
                "human_text": summary,
            }
        }

        context.mark_completed()

        # Build result object following the new pattern

        result = self.create_result(
            data={"agg_summary": agg_summary},
            usecase=self.name,
            category=self.category,
            context=context,
        )

        return result

    def _check_alerts(
        self, summary: dict, frame_number: Any, config: LicensePlateConfig
    ) -> List[Dict]:
        """
        Check if any alert thresholds are exceeded and return alert dicts.
        """

        def get_trend(data, lookback=900, threshold=0.6):
            """
            Determine if the trend is ascending or descending based on actual value progression.
            Now works with values 0,1,2,3 (not just binary).
            """
            window = data[-lookback:] if len(data) >= lookback else data
            if len(window) < 2:
                return True  # not enough data to determine trend
            increasing = 0
            total = 0
            for i in range(1, len(window)):
                if window[i] >= window[i - 1]:
                    increasing += 1
                total += 1
            ratio = increasing / total
            if ratio >= threshold:
                return True
            elif ratio <= (1 - threshold):
                return False

        frame_key = str(frame_number) if frame_number is not None else "current_frame"
        alerts = []
        total_detections = summary.get(
            "total_count", 0
        )  # CURRENT combined total count of all classes
        total_counts_dict = summary.get(
            "total_counts", {}
        )  # TOTAL cumulative counts per class
        cumulative_total = (
            sum(total_counts_dict.values()) if total_counts_dict else 0
        )  # TOTAL combined cumulative count
        per_category_count = summary.get(
            "per_category_count", {}
        )  # CURRENT count per class

        if not config.alert_config:
            return alerts

        total = summary.get("total_count", 0)
        # self._ascending_alert_list
        if (
            hasattr(config.alert_config, "count_thresholds")
            and config.alert_config.count_thresholds
        ):

            for category, threshold in config.alert_config.count_thresholds.items():
                if category == "all" and total > threshold:

                    alerts.append(
                        {
                            "alert_type": (
                                getattr(config.alert_config, "alert_type", ["Default"])
                                if hasattr(config.alert_config, "alert_type")
                                else ["Default"]
                            ),
                            "alert_id": "alert_" + category + "_" + frame_key,
                            "incident_category": self.CASE_TYPE,
                            "threshold_level": threshold,
                            "ascending": get_trend(
                                self._ascending_alert_list, lookback=900, threshold=0.8
                            ),
                            "settings": {
                                t: v
                                for t, v in zip(
                                    (
                                        getattr(
                                            config.alert_config,
                                            "alert_type",
                                            ["Default"],
                                        )
                                        if hasattr(config.alert_config, "alert_type")
                                        else ["Default"]
                                    ),
                                    (
                                        getattr(
                                            config.alert_config, "alert_value", ["JSON"]
                                        )
                                        if hasattr(config.alert_config, "alert_value")
                                        else ["JSON"]
                                    ),
                                )
                            },
                        }
                    )
                elif category in summary.get("per_category_count", {}):
                    count = summary.get("per_category_count", {})[category]
                    if count > threshold:  # Fixed logic: alert when EXCEEDING threshold
                        alerts.append(
                            {
                                "alert_type": (
                                    getattr(
                                        config.alert_config, "alert_type", ["Default"]
                                    )
                                    if hasattr(config.alert_config, "alert_type")
                                    else ["Default"]
                                ),
                                "alert_id": "alert_" + category + "_" + frame_key,
                                "incident_category": self.CASE_TYPE,
                                "threshold_level": threshold,
                                "ascending": get_trend(
                                    self._ascending_alert_list,
                                    lookback=900,
                                    threshold=0.8,
                                ),
                                "settings": {
                                    t: v
                                    for t, v in zip(
                                        (
                                            getattr(
                                                config.alert_config,
                                                "alert_type",
                                                ["Default"],
                                            )
                                            if hasattr(
                                                config.alert_config, "alert_type"
                                            )
                                            else ["Default"]
                                        ),
                                        (
                                            getattr(
                                                config.alert_config,
                                                "alert_value",
                                                ["JSON"],
                                            )
                                            if hasattr(
                                                config.alert_config, "alert_value"
                                            )
                                            else ["JSON"]
                                        ),
                                    )
                                },
                            }
                        )
        else:
            pass
        return alerts

    def _generate_incidents(
        self,
        counting_summary: Dict,
        alerts: List,
        config: LicensePlateConfig,
        frame_number: Optional[int] = None,
        stream_info: Optional[Dict[str, Any]] = None,
    ) -> List[Dict]:
        """Generate structured events for the output format with frame-based keys."""

        # Use frame number as key, fallback to 'current_frame' if not available
        frame_key = str(frame_number) if frame_number is not None else "current_frame"
        incidents = []
        total_detections = counting_summary.get("total_count", 0)
        current_timestamp = self._get_current_timestamp_str(stream_info)
        camera_info = self.get_camera_info_from_stream(stream_info)

        self._ascending_alert_list = (
            self._ascending_alert_list[-900:]
            if len(self._ascending_alert_list) > 900
            else self._ascending_alert_list
        )

        if total_detections > 0:
            # Determine event level based on thresholds
            level = "low"
            intensity = 5.0
            start_timestamp = self._get_start_timestamp_str(stream_info)
            if start_timestamp and self.current_incident_end_timestamp == "N/A":
                self.current_incident_end_timestamp = "Incident still active"
            elif (
                start_timestamp
                and self.current_incident_end_timestamp == "Incident still active"
            ):
                if (
                    len(self._ascending_alert_list) >= 15
                    and sum(self._ascending_alert_list[-15:]) / 15 < 1.5
                ):
                    self.current_incident_end_timestamp = current_timestamp
            elif (
                self.current_incident_end_timestamp != "Incident still active"
                and self.current_incident_end_timestamp != "N/A"
            ):
                self.current_incident_end_timestamp = "N/A"

            if config.alert_config and config.alert_config.count_thresholds:
                threshold = config.alert_config.count_thresholds.get("all", 15)
                intensity = min(10.0, (total_detections / threshold) * 10)

                if intensity >= 9:
                    level = "critical"
                    self._ascending_alert_list.append(3)
                elif intensity >= 7:
                    level = "significant"
                    self._ascending_alert_list.append(2)
                elif intensity >= 5:
                    level = "medium"
                    self._ascending_alert_list.append(1)
                else:
                    level = "low"
                    self._ascending_alert_list.append(0)
            else:
                if total_detections > 30:
                    level = "critical"
                    intensity = 10.0
                    self._ascending_alert_list.append(3)
                elif total_detections > 25:
                    level = "significant"
                    intensity = 9.0
                    self._ascending_alert_list.append(2)
                elif total_detections > 15:
                    level = "medium"
                    intensity = 7.0
                    self._ascending_alert_list.append(1)
                else:
                    level = "low"
                    intensity = min(10.0, total_detections / 3.0)
                    self._ascending_alert_list.append(0)

            # Generate human text in new format
            human_text_lines = [f"INCIDENTS DETECTED @ {current_timestamp}:"]
            human_text_lines.append(f"\tSeverity Level: {(self.CASE_TYPE,level)}")
            human_text = "\n".join(human_text_lines)

            alert_settings = []
            if config.alert_config and hasattr(config.alert_config, "alert_type"):
                alert_settings.append(
                    {
                        "alert_type": (
                            getattr(config.alert_config, "alert_type", ["Default"])
                            if hasattr(config.alert_config, "alert_type")
                            else ["Default"]
                        ),
                        "incident_category": self.CASE_TYPE,
                        "threshold_level": (
                            config.alert_config.count_thresholds
                            if hasattr(config.alert_config, "count_thresholds")
                            else {}
                        ),
                        "ascending": True,
                        "settings": {
                            t: v
                            for t, v in zip(
                                (
                                    getattr(
                                        config.alert_config, "alert_type", ["Default"]
                                    )
                                    if hasattr(config.alert_config, "alert_type")
                                    else ["Default"]
                                ),
                                (
                                    getattr(
                                        config.alert_config, "alert_value", ["JSON"]
                                    )
                                    if hasattr(config.alert_config, "alert_value")
                                    else ["JSON"]
                                ),
                            )
                        },
                    }
                )

            event = self.create_incident(
                incident_id=self.CASE_TYPE + "_" + str(frame_number),
                incident_type=self.CASE_TYPE,
                severity_level=level,
                human_text=human_text,
                camera_info=camera_info,
                alerts=alerts,
                alert_settings=alert_settings,
                start_time=start_timestamp,
                end_time=self.current_incident_end_timestamp,
                level_settings={"low": 1, "medium": 3, "significant": 4, "critical": 7},
            )
            incidents.append(event)

        else:
            self._ascending_alert_list.append(0)
            incidents.append({})

        return incidents

    def _generate_tracking_stats(
        self,
        counting_summary: Dict,
        alerts: Any,
        config: LicensePlateConfig,
        frame_number: Optional[int] = None,
        stream_info: Optional[Dict[str, Any]] = None,
    ) -> List[Dict]:
        """Generate structured tracking stats for the output format with frame-based keys, including track_ids_info and detections with masks."""
        # frame_key = str(frame_number) if frame_number is not None else "current_frame"
        # tracking_stats = [{frame_key: []}]
        # frame_tracking_stats = tracking_stats[0][frame_key]
        tracking_stats = []

        total_detections = counting_summary.get("total_count", 0)
        total_counts = counting_summary.get("total_counts", {})
        cumulative_total = sum(total_counts.values()) if total_counts else 0
        per_category_count = counting_summary.get("per_category_count", {})

        track_ids_info = self._get_track_ids_info(
            counting_summary.get("detections", [])
        )

        current_timestamp = self._get_current_timestamp_str(
            stream_info, precision=False
        )
        start_timestamp = self._get_start_timestamp_str(stream_info, precision=False)

        # Create high precision timestamps for input_timestamp and reset_timestamp
        high_precision_start_timestamp = self._get_current_timestamp_str(
            stream_info, precision=True
        )
        high_precision_reset_timestamp = self._get_start_timestamp_str(
            stream_info, precision=True
        )

        camera_info = self.get_camera_info_from_stream(stream_info)
        human_text_lines = []

        # CURRENT FRAME section
        human_text_lines.append(f"CURRENT FRAME @ {current_timestamp}:")
        if total_detections > 0:
            category_counts = [
                f"{count} {cat}" for cat, count in per_category_count.items()
            ]
            if len(category_counts) == 1:
                detection_text = category_counts[0] + " detected"
            elif len(category_counts) == 2:
                detection_text = (
                    f"{category_counts[0]} and {category_counts[1]} detected"
                )
            else:
                detection_text = f"{', '.join(category_counts[:-1])}, and {category_counts[-1]} detected"
            human_text_lines.append(f"\t- {detection_text}")
        else:
            human_text_lines.append(f"\t- No detections")

        human_text_lines.append("")  # spacing

        # TOTAL SINCE section
        human_text_lines.append(f"TOTAL SINCE {start_timestamp}:")
        human_text_lines.append(f"\t- Total Detected: {cumulative_total}")
        # Add category-wise counts
        if total_counts:
            for cat, count in total_counts.items():
                if count > 0:  # Only include categories with non-zero counts
                    human_text_lines.append(f"\t- {cat}: {count}")
        # Build current_counts array in expected format
        current_counts = []
        for cat, count in per_category_count.items():
            if (
                count > 0 or total_detections > 0
            ):  # Include even if 0 when there are detections
                current_counts.append({"category": cat, "count": count})

        human_text = "\n".join(human_text_lines)

        # Include detections with masks from counting_summary
        # Prepare detections without confidence scores (as per eg.json)
        detections = []
        for detection in counting_summary.get("detections", []):
            bbox = detection.get("bounding_box", {})
            category = detection.get("category", "person")
            # Include segmentation if available (like in eg.json)
            if detection.get("masks"):
                segmentation = detection.get("masks", [])
                detection_obj = self.create_detection_object(
                    category, bbox, segmentation=segmentation
                )
            elif detection.get("segmentation"):
                segmentation = detection.get("segmentation")
                detection_obj = self.create_detection_object(
                    category, bbox, segmentation=segmentation
                )
            elif detection.get("mask"):
                segmentation = detection.get("mask")
                detection_obj = self.create_detection_object(
                    category, bbox, segmentation=segmentation
                )
            else:
                detection_obj = self.create_detection_object(category, bbox)
            detections.append(detection_obj)

        # Build alert_settings array in expected format
        alert_settings = []
        if config.alert_config and hasattr(config.alert_config, "alert_type"):
            alert_settings.append(
                {
                    "alert_type": (
                        getattr(config.alert_config, "alert_type", ["Default"])
                        if hasattr(config.alert_config, "alert_type")
                        else ["Default"]
                    ),
                    "incident_category": self.CASE_TYPE,
                    "threshold_level": (
                        config.alert_config.count_thresholds
                        if hasattr(config.alert_config, "count_thresholds")
                        else {}
                    ),
                    "ascending": True,
                    "settings": {
                        t: v
                        for t, v in zip(
                            (
                                getattr(config.alert_config, "alert_type", ["Default"])
                                if hasattr(config.alert_config, "alert_type")
                                else ["Default"]
                            ),
                            (
                                getattr(config.alert_config, "alert_value", ["JSON"])
                                if hasattr(config.alert_config, "alert_value")
                                else ["JSON"]
                            ),
                        )
                    },
                }
            )

        if alerts:
            for alert in alerts:
                human_text_lines.append(
                    f"Alerts: {alert.get('settings', {})} sent @ {current_timestamp}"
                )
        else:
            human_text_lines.append("Alerts: None")

        human_text = "\n".join(human_text_lines)
        reset_settings = [
            {"interval_type": "daily", "reset_time": {"value": 9, "time_unit": "hour"}}
        ]

        tracking_stat = self.create_tracking_stats(
            total_counts=total_counts,
            current_counts=current_counts,
            detections=detections,
            human_text=human_text,
            camera_info=camera_info,
            alerts=alerts,
            alert_settings=alert_settings,
            reset_settings=reset_settings,
            start_time=high_precision_start_timestamp,
            reset_time=high_precision_reset_timestamp,
        )

        tracking_stats.append(tracking_stat)
        return tracking_stats

    def _generate_business_analytics(
        self,
        counting_summary: Dict,
        zone_analysis: Dict,
        config: LicensePlateConfig,
        stream_info: Optional[Dict[str, Any]] = None,
        is_empty=False,
    ) -> List[Dict]:
        """Generate standardized business analytics for the agg_summary structure."""
        if is_empty:
            return []

        # -----IF YOUR USECASE NEEDS BUSINESS ANALYTICS, YOU CAN USE THIS FUNCTION------#
        # camera_info = self.get_camera_info_from_stream(stream_info)
        # business_analytics = self.create_business_analytics(nalysis_name, statistics,
        #                          human_text, camera_info=camera_info, alerts=alerts, alert_settings=alert_settings,
        #                          reset_settings)
        # return business_analytics

    def _generate_summary(
        self,
        summary: dict,
        incidents: List,
        tracking_stats: List,
        business_analytics: List,
        alerts: List,
    ) -> List[str]:
        """
        Generate a human_text string for the tracking_stat, incident, business analytics and alerts.
        """
        lines = {}
        lines["Application Name"] = self.CASE_TYPE
        lines["Application Version"] = self.CASE_VERSION
        if len(incidents) > 0:
            lines["Incidents:"] = (
                f"\n\t{incidents[0].get('human_text', 'No incidents detected')}\n"
            )
        if len(tracking_stats) > 0:
            lines["Tracking Statistics:"] = (
                f"\t{tracking_stats[0].get('human_text', 'No tracking statistics detected')}\n"
            )
        if len(business_analytics) > 0:
            lines["Business Analytics:"] = (
                f"\t{business_analytics[0].get('human_text', 'No business analytics detected')}\n"
            )

        if (
            len(incidents) == 0
            and len(tracking_stats) == 0
            and len(business_analytics) == 0
        ):
            lines["Summary"] = "No Summary Data"

        return [lines]

    def _count_categories(self, detections: list, config: LicensePlateConfig) -> dict:
        """
        Count the number of detections per category and return a summary dict.
        The detections list is expected to have 'track_id' (from tracker), 'category', 'bounding_box', 'masks', etc.
        Output structure will include 'track_id' and 'masks' for each detection as per AdvancedTracker output.
        """
        counts = {}
        valid_detections = []
        for det in detections:
            cat = det.get("category", "unknown")
            if not all(
                k in det for k in ["category", "confidence", "bounding_box"]
            ):  # Validate required fields
                self.logger.warning(f"Skipping invalid detection: {det}")
                continue
            counts[cat] = counts.get(cat, 0) + 1
            valid_detections.append(
                {
                    "bounding_box": det.get("bounding_box"),
                    "category": det.get("category"),
                    "confidence": det.get("confidence"),
                    "track_id": det.get("track_id"),
                    "frame_id": det.get("frame_id"),
                    "masks": det.get(
                        "masks", det.get("mask", [])
                    ),  # Include masks, fallback to empty list
                }
            )
        self.logger.debug(f"Valid detections after filtering: {len(valid_detections)}")
        return {
            "total_count": sum(counts.values()),
            "per_category_count": counts,
            "detections": valid_detections,
        }

    def _get_track_ids_info(self, detections: list) -> Dict[str, Any]:
        """
        Get detailed information about track IDs (per frame).
        """
        # Collect all track_ids in this frame
        frame_track_ids = set()
        for det in detections:
            tid = det.get("track_id")
            if tid is not None:
                frame_track_ids.add(tid)
        # Use persistent total set for unique counting
        total_track_ids = set()
        for s in getattr(self, "_per_category_total_track_ids", {}).values():
            total_track_ids.update(s)
        return {
            "total_count": len(total_track_ids),
            "current_frame_count": len(frame_track_ids),
            "total_unique_track_ids": len(total_track_ids),
            "current_frame_track_ids": list(frame_track_ids),
            "last_update_time": time.time(),
            "total_frames_processed": getattr(self, "_total_frame_counter", 0),
        }

    def _update_tracking_state(self, detections: list):
        """
        Track unique categories track_ids per category for total count after tracking.
        Applies canonical ID merging to avoid duplicate counting when the underlying
        tracker loses an object temporarily and assigns a new ID.
        """
        # Lazily initialise storage dicts
        if not hasattr(self, "_per_category_total_track_ids"):
            self._per_category_total_track_ids = {
                cat: set() for cat in self.target_categories
            }
        self._current_frame_track_ids = {cat: set() for cat in self.target_categories}

        for det in detections:
            cat = det.get("category")
            raw_track_id = det.get("track_id")
            if cat not in self.target_categories or raw_track_id is None:
                continue
            bbox = det.get("bounding_box", det.get("bbox"))
            canonical_id = self._merge_or_register_track(raw_track_id, bbox)
            # Propagate canonical ID back to detection so downstream logic uses it
            det["track_id"] = canonical_id

            self._per_category_total_track_ids.setdefault(cat, set()).add(canonical_id)
            self._current_frame_track_ids[cat].add(canonical_id)

    def get_total_counts(self):
        """
        Return total unique track_id count for each category.
        """
        return {
            cat: len(ids)
            for cat, ids in getattr(self, "_per_category_total_track_ids", {}).items()
        }

    def _format_timestamp_for_video(self, timestamp: float) -> str:
        """Format timestamp for video chunks (HH:MM:SS.ms format)."""
        hours = int(timestamp // 3600)
        minutes = int((timestamp % 3600) // 60)
        seconds = round(float(timestamp % 60), 2)
        return f"{hours:02d}:{minutes:02d}:{seconds:.1f}"

    def _format_timestamp_for_stream(self, timestamp: float) -> str:
        """Format timestamp for streams (YYYY:MM:DD HH:MM:SS format)."""
        dt = datetime.fromtimestamp(timestamp, tz=timezone.utc)
        return dt.strftime("%Y:%m:%d %H:%M:%S")

    def _get_current_timestamp_str(
        self,
        stream_info: Optional[Dict[str, Any]],
        precision=False,
        frame_id: Optional[str] = None,
    ) -> str:
        """Get formatted current timestamp based on stream type."""
        if not stream_info:
            return "00:00:00.00"
        # is_video_chunk = stream_info.get("input_settings", {}).get("is_video_chunk", False)
        if precision:
            if stream_info.get("input_settings", {}).get("start_frame", "na") != "na":
                if frame_id:
                    start_time = int(frame_id) / stream_info.get(
                        "input_settings", {}
                    ).get("original_fps", 30)
                else:
                    start_time = stream_info.get("input_settings", {}).get(
                        "start_frame", 30
                    ) / stream_info.get("input_settings", {}).get("original_fps", 30)
                stream_time_str = self._format_timestamp_for_video(start_time)
                return stream_time_str
            else:
                return datetime.now(timezone.utc).strftime("%Y-%m-%d-%H:%M:%S.%f UTC")

        if stream_info.get("input_settings", {}).get("start_frame", "na") != "na":
            if frame_id:
                start_time = int(frame_id) / stream_info.get("input_settings", {}).get(
                    "original_fps", 30
                )
            else:
                start_time = stream_info.get("input_settings", {}).get(
                    "start_frame", 30
                ) / stream_info.get("input_settings", {}).get("original_fps", 30)
            stream_time_str = self._format_timestamp_for_video(start_time)
            return stream_time_str
        else:
            # For streams, use stream_time from stream_info
            stream_time_str = (
                stream_info.get("input_settings", {})
                .get("stream_info", {})
                .get("stream_time", "")
            )
            if stream_time_str:
                # Parse the high precision timestamp string to get timestamp
                try:
                    # Remove " UTC" suffix and parse
                    timestamp_str = stream_time_str.replace(" UTC", "")
                    dt = datetime.strptime(timestamp_str, "%Y-%m-%d-%H:%M:%S.%f")
                    timestamp = dt.replace(tzinfo=timezone.utc).timestamp()
                    return self._format_timestamp_for_stream(timestamp)
                except:
                    # Fallback to current time if parsing fails
                    return self._format_timestamp_for_stream(time.time())
            else:
                return self._format_timestamp_for_stream(time.time())

    def _get_start_timestamp_str(
        self, stream_info: Optional[Dict[str, Any]], precision=False
    ) -> str:
        """Get formatted start timestamp for 'TOTAL SINCE' based on stream type."""
        if not stream_info:
            return "00:00:00"
        if precision:
            if stream_info.get("input_settings", {}).get("start_frame", "na") != "na":
                return "00:00:00"
            else:
                return datetime.now(timezone.utc).strftime("%Y-%m-%d-%H:%M:%S.%f UTC")

        if stream_info.get("input_settings", {}).get("start_frame", "na") != "na":
            # If video format, start from 00:00:00
            return "00:00:00"
        else:
            # For streams, use tracking start time or current time with minutes/seconds reset
            if self._tracking_start_time is None:
                # Try to extract timestamp from stream_time string
                stream_time_str = (
                    stream_info.get("input_settings", {})
                    .get("stream_info", {})
                    .get("stream_time", "")
                )
                if stream_time_str:
                    try:
                        # Remove " UTC" suffix and parse
                        timestamp_str = stream_time_str.replace(" UTC", "")
                        dt = datetime.strptime(timestamp_str, "%Y-%m-%d-%H:%M:%S.%f")
                        self._tracking_start_time = dt.replace(
                            tzinfo=timezone.utc
                        ).timestamp()
                    except:
                        # Fallback to current time if parsing fails
                        self._tracking_start_time = time.time()
                else:
                    self._tracking_start_time = time.time()

            dt = datetime.fromtimestamp(self._tracking_start_time, tz=timezone.utc)
            # Reset minutes and seconds to 00:00 for "TOTAL SINCE" format
            dt = dt.replace(minute=0, second=0, microsecond=0)
            return dt.strftime("%Y:%m:%d %H:%M:%S")

    # ------------------------------------------------------------------ #
    # Helper to merge masks back into detections                        #
    # ------------------------------------------------------------------ #
    def _attach_masks_to_detections(
        self,
        processed_detections: List[Dict[str, Any]],
        raw_detections: List[Dict[str, Any]],
        iou_threshold: float = 0.5,
    ) -> List[Dict[str, Any]]:
        """
        Attach segmentation masks from the original `raw_detections` list to the
        `processed_detections` list returned after smoothing/tracking.

        Matching between detections is performed using Intersection-over-Union
        (IoU) of the bounding boxes. For each processed detection we select the
        raw detection with the highest IoU above `iou_threshold` and copy its
        `masks` (or `mask`) field. If no suitable match is found, the detection
        keeps an empty list for `masks` to maintain a consistent schema.
        """

        if not processed_detections or not raw_detections:
            # Nothing to do – ensure masks key exists for downstream logic.
            for det in processed_detections:
                det.setdefault("masks", [])
            return processed_detections

        # Track which raw detections have already been matched to avoid
        # assigning the same mask to multiple processed detections.
        used_raw_indices = set()

        for det in processed_detections:
            best_iou = 0.0
            best_idx = None

            for idx, raw_det in enumerate(raw_detections):
                if idx in used_raw_indices:
                    continue

                iou = self._compute_iou(
                    det.get("bounding_box"), raw_det.get("bounding_box")
                )
                if iou > best_iou:
                    best_iou = iou
                    best_idx = idx

            if best_idx is not None and best_iou >= iou_threshold:
                raw_det = raw_detections[best_idx]
                masks = raw_det.get("masks", raw_det.get("mask"))
                if masks is not None:
                    det["masks"] = masks
                used_raw_indices.add(best_idx)
            else:
                # No adequate match – default to empty list to keep schema consistent.
                det.setdefault("masks", ["EMPTY"])

        return processed_detections

    def _extract_predictions(self, detections: list) -> List[Dict[str, Any]]:
        """
        Extract prediction details for output (category, confidence, bounding box).
        """
        return [
            {
                "category": det.get("category", "unknown"),
                "confidence": det.get("confidence", 0.0),
                "bounding_box": det.get("bounding_box", {}),
                "mask": det.get("mask", det.get("masks", None)),  # Accept either key
            }
            for det in detections
        ]

    # ------------------------------------------------------------------ #
    # Canonical ID helpers                                               #
    # ------------------------------------------------------------------ #
    def _compute_iou(self, box1: Any, box2: Any) -> float:
        """Compute IoU between two bounding boxes which may be dicts or lists.
        Falls back to 0 when insufficient data is available."""

        # Helper to convert bbox (dict or list) to [x1, y1, x2, y2]
        def _bbox_to_list(bbox):
            if bbox is None:
                return []
            if isinstance(bbox, list):
                return bbox[:4] if len(bbox) >= 4 else []
            if isinstance(bbox, dict):
                if "xmin" in bbox:
                    return [bbox["xmin"], bbox["ymin"], bbox["xmax"], bbox["ymax"]]
                if "x1" in bbox:
                    return [bbox["x1"], bbox["y1"], bbox["x2"], bbox["y2"]]
                # Fallback: first four numeric values
                values = [v for v in bbox.values() if isinstance(v, (int, float))]
                return values[:4] if len(values) >= 4 else []
            return []

        l1 = _bbox_to_list(box1)
        l2 = _bbox_to_list(box2)
        if len(l1) < 4 or len(l2) < 4:
            return 0.0
        x1_min, y1_min, x1_max, y1_max = l1
        x2_min, y2_min, x2_max, y2_max = l2

        # Ensure correct order
        x1_min, x1_max = min(x1_min, x1_max), max(x1_min, x1_max)
        y1_min, y1_max = min(y1_min, y1_max), max(y1_min, y1_max)
        x2_min, x2_max = min(x2_min, x2_max), max(x2_min, x2_max)
        y2_min, y2_max = min(y2_min, y2_max), max(y2_min, y2_max)

        inter_x_min = max(x1_min, x2_min)
        inter_y_min = max(y1_min, y2_min)
        inter_x_max = min(x1_max, x2_max)
        inter_y_max = min(y1_max, y2_max)

        inter_w = max(0.0, inter_x_max - inter_x_min)
        inter_h = max(0.0, inter_y_max - inter_y_min)
        inter_area = inter_w * inter_h

        area1 = (x1_max - x1_min) * (y1_max - y1_min)
        area2 = (x2_max - x2_min) * (y2_max - y2_min)
        union_area = area1 + area2 - inter_area

        return (inter_area / union_area) if union_area > 0 else 0.0

    def _merge_or_register_track(self, raw_id: Any, bbox: Any) -> Any:
        """Return a stable canonical ID for a raw tracker ID, merging fragmented
        tracks when IoU and temporal constraints indicate they represent the
        same physical."""
        if raw_id is None or bbox is None:
            # Nothing to merge
            return raw_id

        now = time.time()

        # Fast path – raw_id already mapped
        if raw_id in self._track_aliases:
            canonical_id = self._track_aliases[raw_id]
            track_info = self._canonical_tracks.get(canonical_id)
            if track_info is not None:
                track_info["last_bbox"] = bbox
                track_info["last_update"] = now
                track_info["raw_ids"].add(raw_id)
            return canonical_id

        # Attempt to merge with an existing canonical track
        for canonical_id, info in self._canonical_tracks.items():
            # Only consider recently updated tracks
            if now - info["last_update"] > self._track_merge_time_window:
                continue
            iou = self._compute_iou(bbox, info["last_bbox"])
            if iou >= self._track_merge_iou_threshold:
                # Merge
                self._track_aliases[raw_id] = canonical_id
                info["last_bbox"] = bbox
                info["last_update"] = now
                info["raw_ids"].add(raw_id)
                return canonical_id

        # No match – register new canonical track
        canonical_id = raw_id
        self._track_aliases[raw_id] = canonical_id
        self._canonical_tracks[canonical_id] = {
            "last_bbox": bbox,
            "last_update": now,
            "raw_ids": {raw_id},
        }
        return canonical_id

    def _format_timestamp(self, timestamp: float) -> str:
        """Format a timestamp for human-readable output."""
        return datetime.fromtimestamp(timestamp, timezone.utc).strftime(
            "%Y-%m-%d %H:%M:%S UTC"
        )

    def _get_tracking_start_time(self) -> str:
        """Get the tracking start time, formatted as a string."""
        if self._tracking_start_time is None:
            return "N/A"
        return self._format_timestamp(self._tracking_start_time)

    def _set_tracking_start_time(self) -> None:
        """Set the tracking start time to the current time."""
        self._tracking_start_time = time.time()
