"""Object detection models for perception system."""

import time
import asyncio
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
from io import BytesIO

import numpy as np
import cv2
from PIL import Image
import structlog

from opencar.perception.utils.nms import non_max_suppression

logger = structlog.get_logger()


class ObjectDetector:
    """Base object detection model."""

    def __init__(
        self,
        num_classes: int = 80,
        confidence_threshold: float = 0.5,
        nms_threshold: float = 0.4,
        device: str = "cpu",
    ):
        """Initialize detector."""
        self.num_classes = num_classes
        self.confidence_threshold = confidence_threshold
        self.nms_threshold = nms_threshold
        self.device = device
        self.model = None
        self.is_initialized = False

        # Mock model for demonstration
        self._mock_model = self._build_model()

    def _build_model(self):
        """Build detection model."""
        # Mock implementation
        return {"model_type": "yolo", "classes": self.num_classes}

    async def initialize(self) -> None:
        """Initialize the model asynchronously."""
        if self.is_initialized:
            return
            
        try:
            logger.info("Initializing object detector...")
            # Simulate model loading time
            await asyncio.sleep(0.1)
            self.model = self._mock_model
            self.is_initialized = True
            logger.info("Object detector initialized successfully")
        except Exception as e:
            logger.error(f"Failed to initialize detector: {str(e)}")
            raise

    async def health_check(self) -> bool:
        """Check if the detector is healthy and ready."""
        return self.is_initialized and self.model is not None

    async def reload(self) -> None:
        """Reload the model."""
        logger.info("Reloading object detector...")
        self.is_initialized = False
        self.model = None
        await self.initialize()
        logger.info("Object detector reloaded successfully")

    async def detect(
        self,
        image_data: Union[bytes, np.ndarray],
        confidence_threshold: Optional[float] = None,
        return_time: bool = False,
    ) -> List[Dict[str, Any]]:
        """Run detection on image."""
        if not self.is_initialized:
            await self.initialize()
            
        start_time = time.time()
        
        try:
            # Convert image data to numpy array if needed
            if isinstance(image_data, bytes):
                image = self._bytes_to_image(image_data)
            else:
                image = image_data
                
            # Use provided threshold or default
            threshold = confidence_threshold or self.confidence_threshold
            
            # Mock detection results - in production this would be actual inference
            detections = [
                {
                    "class_name": "car",
                    "confidence": 0.92,
                    "bbox": {
                        "x1": 100.0,
                        "y1": 200.0,
                        "x2": 300.0,
                        "y2": 400.0
                    },
                    "attributes": {"vehicle_type": "sedan"}
                },
                {
                    "class_name": "person", 
                    "confidence": 0.87,
                    "bbox": {
                        "x1": 400.0,
                        "y1": 300.0,
                        "x2": 550.0,
                        "y2": 600.0
                    },
                    "attributes": {"pedestrian": True}
                },
            ]
            
            # Filter by confidence threshold
            filtered_detections = [
                det for det in detections 
                if det["confidence"] >= threshold
            ]
            
            inference_time = (time.time() - start_time) * 1000  # ms
            
            logger.info(f"Detection completed: {len(filtered_detections)} objects found in {inference_time:.2f}ms")
            
            return filtered_detections
            
        except Exception as e:
            logger.error(f"Detection error: {str(e)}")
            return []

    def _bytes_to_image(self, image_data: bytes) -> np.ndarray:
        """Convert bytes to numpy image array."""
        try:
            # Use PIL to load image from bytes
            image = Image.open(BytesIO(image_data))
            image = image.convert('RGB')
            
            # Convert PIL to OpenCV format
            image_array = np.array(image)
            image_array = cv2.cvtColor(image_array, cv2.COLOR_RGB2BGR)
            
            return image_array
        except Exception as e:
            logger.error(f"Image conversion error: {str(e)}")
            # Return a dummy image
            return np.zeros((480, 640, 3), dtype=np.uint8)

    def _get_class_name(self, class_id: int) -> str:
        """Get class name from ID."""
        # Mock COCO classes
        coco_classes = [
            "person", "bicycle", "car", "motorcycle", "airplane",
            "bus", "train", "truck", "boat", "traffic light",
            "fire hydrant", "stop sign", "parking meter", "bench", "bird",
            "cat", "dog", "horse", "sheep", "cow",
        ]
        if class_id < len(coco_classes):
            return coco_classes[class_id]
        return f"class_{class_id}"


class YOLODetector(ObjectDetector):
    """YOLO-based object detector for real-time performance."""

    def __init__(self, model_size: str = "n", **kwargs):
        """Initialize YOLO detector."""
        self.model_size = model_size
        super().__init__(**kwargs)

    def _build_model(self):
        """Build YOLO model."""
        return {
            "model_type": f"yolov8{self.model_size}",
            "classes": self.num_classes,
            "size": self.model_size
        } 