"""
pothole Monitoring Use Case for Post-Processing

This module provides pothole damage monitoring functionality ,
zone analysis, and alert generation.

"""

from typing import Any, Dict, List, Optional
from dataclasses import asdict
import time
from datetime import datetime, timezone
import copy  # Added for deep copying detections to preserve original masks

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 PotholeConfig(BaseConfig):
    """Configuration for pothole detection use case in pothole 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: ['pothole']
    )

    target_categories: List[str] = field(
        default_factory=lambda: ['pothole']
    )

    alert_config: Optional[AlertConfig] = None

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

        }
    )


class PotholeSegmentationUseCase(BaseProcessor):
    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 = timestamp % 60
        return f"{hours:02d}:{minutes:02d}:{seconds:06.2f}"

    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]]) -> 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 is_video_chunk:
        #     # For video chunks, use video_timestamp from stream_info
        #     video_timestamp = stream_info.get("video_timestamp", 0.0)
        #     return self._format_timestamp_for_video(video_timestamp)
        if stream_info.get("input_settings", {}).get("stream_type", "video_file") == "video_file":
            # If video format, return video timestamp
            stream_time_str = stream_info.get("video_timestamp", "")
            return stream_time_str[:8]
        else:
            # For streams, use stream_time from stream_info
            stream_time_str = 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]]) -> str:
        """Get formatted start timestamp for 'TOTAL SINCE' based on stream type."""
        if not stream_info:
            return "00:00:00"

        is_video_chunk = stream_info.get("input_settings", {}).get("is_video_chunk", False)

        if is_video_chunk:
            # For video chunks, start from 00:00:00
            return "00:00:00"
        elif stream_info.get("input_settings", {}).get("stream_type", "video_file") == "video_file":
            # 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("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')

    """ Monitoring use case with smoothing and alerting."""

    def __init__(self):
        super().__init__("pothole_segmentation")
        self.category = "infrastructure"

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



        # 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

    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, PotholeConfig):
            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

            # Create tracker instance if it doesn't exist (preserves state across frames)
            if self.tracker is None:
                tracker_config = TrackerConfig()
                self.tracker = AdvancedTracker(tracker_config)
                self.logger.info("Initialized AdvancedTracker for Monitoring and tracking")
           
            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).
        # ------------------------------------------------------------------ #
        print("DEBUGGGGGGGGGGGGGGG111")
        print(processed_data)
        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
        print("DEBUGGGGGGGGGGGGGGG2")
        print(processed_data)
        insights = self._generate_insights(counting_summary, config)
        alerts = self._check_alerts(counting_summary, config)
        predictions = self._extract_predictions(processed_data)
        summary = self._generate_summary(counting_summary, alerts)

        # Step: Generate structured events and tracking stats with frame-based keys
        events_list = self._generate_events(counting_summary, alerts, config, frame_number, stream_info)
        tracking_stats_list = self._generate_tracking_stats(counting_summary, insights, summary, config, frame_number,
                                                            stream_info)

        # Extract frame-based dictionaries from the lists
        events = events_list[0] if events_list else {}
        tracking_stats = tracking_stats_list[0] if tracking_stats_list else {}

        context.mark_completed()

        # Build result object
        result = self.create_result(
            data={
                "counting_summary": counting_summary,
                "general_counting_summary": general_counting_summary,
                "alerts": alerts,
                "total_detections": counting_summary.get("total_count", 0),
                "events": events,
                "tracking_stats": tracking_stats,
            },
            usecase=self.name,
            category=self.category,
            context=context
        )
        result.summary = summary
        result.insights = insights
        result.predictions = predictions
        return result





    def _generate_events(self, counting_summary: Dict, alerts: List, config: PotholeConfig,
                         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."""
        from datetime import datetime, timezone

        # 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"
        events = [{frame_key: []}]
        frame_events = events[0][frame_key]
        total_detections = counting_summary.get("total_count", 0)

        if total_detections > 0:
            # Determine event level based on thresholds
            level = "info"
            intensity = 5.0
            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 >= 7:
                    level = "critical"
                elif intensity >= 5:
                    level = "warning"
                else:
                    level = "info"
            else:
                if total_detections > 25:
                    level = "critical"
                    intensity = 9.0
                elif total_detections > 15:
                    level = "warning"
                    intensity = 7.0
                else:
                    level = "info"
                    intensity = min(10.0, total_detections / 3.0)

            # Generate human text in new format
            human_text_lines = ["EVENTS DETECTED:"]
            human_text_lines.append(f"    - {total_detections}  detected [INFO]")
            human_text = "\n".join(human_text_lines)

            event = {
                "type": "pothole_segmentation",
                "stream_time": datetime.now(timezone.utc).strftime("%Y-%m-%d-%H:%M:%S UTC"),
                "level": level,
                "intensity": round(intensity, 1),
                "config": {
                    "min_value": 0,
                    "max_value": 10,
                    "level_settings": {"info": 2, "warning": 5, "critical": 7}
                },
                "application_name": "pothole detection System",
                "application_version": "1.2",
                "location_info": None,
                "human_text": human_text
            }
            frame_events.append(event)

        # Add alert events
        for alert in alerts:
            total_detections = counting_summary.get("total_count", 0)
            intensity_message = "ALERT: Low congestion in the scene"
            if config.alert_config and config.alert_config.count_thresholds:
                threshold = config.alert_config.count_thresholds.get("all", 15)
                percentage = (total_detections / threshold) * 100 if threshold > 0 else 0
                if percentage < 20:
                    intensity_message = "ALERT: Low congestion in the scene"
                elif percentage <= 50:
                    intensity_message = "ALERT: Moderate congestion in the scene"
                elif percentage <= 70:
                    intensity_message = "ALERT: Heavy congestion in the scene"
                else:
                    intensity_message = "ALERT: Severe congestion in the scene"
            else:
                if total_detections > 15:
                    intensity_message = "ALERT: Heavy congestion in the scene"
                elif total_detections == 1:
                    intensity_message = "ALERT: Low congestion in the scene"
                else:
                    intensity_message = "ALERT: Moderate congestion in the scene"

            alert_event = {
                "type": alert.get("type", "congestion_alert"),
                "stream_time": datetime.now(timezone.utc).strftime("%Y-%m-%d-%H:%M:%S UTC"),
                "level": alert.get("severity", "warning"),
                "intensity": 8.0,
                "config": {
                    "min_value": 0,
                    "max_value": 10,
                    "level_settings": {"info": 2, "warning": 5, "critical": 7}
                },
                "application_name": "Congestion Alert System",
                "application_version": "1.2",
                "location_info": alert.get("zone"),
                "human_text": f"{datetime.now(timezone.utc).strftime('%Y-%m-%d-%H:%M:%S UTC')} : {intensity_message}"
            }
            frame_events.append(alert_event)

        return events

    def _generate_tracking_stats(
            self,
            counting_summary: Dict,
            insights: List[str],
            summary: str,
            config: PotholeConfig,
            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]

        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)
        start_timestamp = self._get_start_timestamp_str(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}")

        human_text = "\n".join(human_text_lines)

        # Include detections with masks from counting_summary
        detections = [
            {
                "category": det.get("category"),
                "confidence": det.get("confidence"),
                "bounding_box": det.get("bounding_box"),
                "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
            }
            for det in counting_summary.get("detections", [])
        ]

        tracking_stat = {
            "type": "pothole_segmentation",
            "category": "infrastructure",
            "count": total_detections,
            "insights": insights,
            "summary": summary,
            "timestamp": datetime.now(timezone.utc).strftime('%Y-%m-%d-%H:%M:%S UTC'),
            "human_text": human_text,
            "track_ids_info": track_ids_info,
            "global_frame_offset": getattr(self, '_global_frame_offset', 0),
            "local_frame_id": frame_key,
            "detections": detections  # Add detections with masks
        }

        frame_tracking_stats.append(tracking_stat)
        return tracking_stats

    def _count_categories(self, detections: list, config: PotholeConfig) -> 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
        }

    # ------------------------------------------------------------------ #
    # 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

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

    def _generate_insights(self, summary: dict, config: PotholeConfig) -> List[str]:
        """
        Generate human-readable insights for each category.
        """
        insights = []
        per_cat = summary.get("per_category_count", {})
        total_detections = summary.get("total_count", 0)

        if total_detections == 0:
            insights.append("No detections in the scene")
            return insights
        insights.append(f"EVENT: Detected {total_detections}  in the scene")
        # Intensity calculation based on threshold percentage
        intensity_threshold = None
        if (config.alert_config and
                config.alert_config.count_thresholds and
                "all" in config.alert_config.count_thresholds):
            intensity_threshold = config.alert_config.count_thresholds["all"]

        if intensity_threshold is not None:
            # Calculate percentage relative to threshold
            percentage = (total_detections / intensity_threshold) * 100

            if percentage < 20:
                insights.append(f"INTENSITY: Low congestion in the scene ({percentage:.1f}% of capacity)")
            elif percentage <= 50:
                insights.append(f"INTENSITY: Moderate congestion in the scene ({percentage:.1f}% of capacity)")
            elif percentage <= 70:
                insights.append(f"INTENSITY:  Heavy congestion in the scene ({percentage:.1f}% of capacity)")
            else:
                insights.append(f"INTENSITY: Severe congestion in the scene ({percentage:.1f}% of capacity)")


        for cat, count in per_cat.items():
            display = self.CATEGORY_DISPLAY.get(cat, cat)
            insights.append(f"{display}:{count}")
        return insights

    def _check_alerts(self, summary: dict, config: PotholeConfig) -> List[Dict]:
        """
        Check if any alert thresholds are exceeded and return alert dicts.
        """
        alerts = []
        if not config.alert_config:
            return alerts
        total = summary.get("total_count", 0)
        if config.alert_config.count_thresholds:
            for category, threshold in config.alert_config.count_thresholds.items():
                if category == "all" and total >= threshold:
                    timestamp = datetime.now(timezone.utc).strftime('%Y-%m-%d-%H:%M:%S UTC')
                    alert_description = f"detections count ({total}) exceeds threshold ({threshold})"
                    alerts.append({
                        "type": "count_threshold",
                        "severity": "warning",
                        "message": f"Total detections count ({total}) exceeds threshold ({threshold})",
                        "category": category,
                        "current_count": total,
                        "threshold": threshold
                    })
                elif category in summary.get("per_category_count", {}):
                    count = summary.get("per_category_count", {})[category]
                    if count >= threshold:
                        alerts.append({
                            "type": "count_threshold",
                            "severity": "warning",
                            "message": f"{category} count ({count}) exceeds threshold ({threshold})",
                            "category": category,
                            "current_count": count,
                            "threshold": threshold
                        })
        return alerts

    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
        ]

    def _generate_summary(self, summary: dict, alerts: List) -> str:
        """
        Generate a human_text string for the result, including per-category insights if available.
        Adds a tab before each  label for better formatting.
        Also always includes the cumulative count so far.
        """
        total = summary.get("total_count", 0)
        per_cat = summary.get("per_category_count", {})
        cumulative = summary.get("total_counts", {})
        cumulative_total = sum(cumulative.values()) if cumulative else 0
        lines = []
        if total > 0:
            lines.append(f"{total} detections")
            if per_cat:
                lines.append("detections:")
                for cat, count in per_cat.items():
                    lines.append(f"\t{cat}:{count}")
        else:
            lines.append("No  detections")
        lines.append(f"Total detections: {cumulative_total}")
        if alerts:
            lines.append(f"{len(alerts)} alert(s)")
        return "\n".join(lines)

    # ------------------------------------------------------------------ #
    # 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()
