"""
Advanced Computer Vision Processor for Bleu.js
"""

import asyncio
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional

import cv2
import numpy as np
import structlog
import tensorflow as tf

# Constants
VISION_PROCESSOR_NOT_INITIALIZED = "Vision processor not initialized"


@dataclass
class VisionConfig:
    """Configuration for vision processing."""

    max_image_size: int = 1024
    channels: int = 3
    confidence_threshold: float = 0.5
    model_path: str = "models/vision"
    use_quantum: bool = True
    batch_size: int = 32
    num_classes: int = 1000
    feature_dim: int = 2048


class VisionProcessor:
    """Advanced computer vision processor with quantum-enhanced capabilities."""

    def __init__(self, config: Optional[VisionConfig] = None):
        self.config = config or VisionConfig()
        self.logger = structlog.get_logger()
        self.models = {}
        self.initialized = False
        self.model = None

    def initialize(self):
        """Initialize the vision processor."""
        try:
            self.model = self._load_model()
            self.initialized = True
            self.logger.info("Vision processor initialized successfully")
        except Exception as e:
            self.logger.error(f"Failed to initialize vision processor: {str(e)}")
            raise

    def process_image(self, image: np.ndarray) -> Dict:
        """Process image with all vision capabilities."""
        if not self.initialized:
            raise RuntimeError(VISION_PROCESSOR_NOT_INITIALIZED)

        try:
            # Preprocess image
            processed_image = self._preprocess_image(image)

            # Run parallel inference
            results = self._run_parallel_inference(processed_image)

            # Post-process results
            return self._post_process_results(results)

        except Exception as e:
            self.logger.error(f"Failed to process image: {str(e)}")
            raise

    def detect_objects(self, image: np.ndarray) -> List[Dict]:
        """Detect objects in image with quantum-enhanced accuracy."""
        if not self.initialized:
            raise RuntimeError(VISION_PROCESSOR_NOT_INITIALIZED)

        try:
            # Preprocess image
            processed_image = self._preprocess_image(image)

            # Get object detection predictions
            predictions = self.models["object_detection"].predict(processed_image)

            # Apply quantum enhancement if enabled
            if self.config.use_quantum:
                predictions = self._apply_quantum_enhancements(predictions)

            # Process predictions
            return self._process_object_detections(predictions)

        except Exception as e:
            self.logger.error(f"Failed to detect objects: {str(e)}")
            raise

    def analyze_scene(self, image: np.ndarray) -> Dict:
        """Analyze scene with advanced understanding."""
        if not self.initialized:
            raise RuntimeError(VISION_PROCESSOR_NOT_INITIALIZED)

        try:
            # Preprocess image
            processed_image = self._preprocess_image(image)

            # Get scene analysis predictions
            predictions = self.models["scene_recognition"].predict(processed_image)

            # Apply quantum enhancement if enabled
            if self.config.use_quantum:
                predictions = self._apply_quantum_enhancements(predictions)

            # Process predictions
            return self._process_scene_analysis(predictions)

        except Exception as e:
            self.logger.error(f"Failed to analyze scene: {str(e)}")
            raise

    def detect_faces(self, image: np.ndarray) -> List[Dict]:
        """Detect and analyze faces in image."""
        if not self.initialized:
            raise RuntimeError(VISION_PROCESSOR_NOT_INITIALIZED)

        try:
            # Preprocess image
            processed_image = self._preprocess_image(image)

            # Get face detection predictions
            predictions = self.models["face_detection"].predict(processed_image)

            # Apply quantum enhancement if enabled
            if self.config.use_quantum:
                predictions = self._apply_quantum_enhancements(predictions)

            # Process predictions
            return self._process_face_detections(predictions)

        except Exception as e:
            self.logger.error(f"Failed to detect faces: {str(e)}")
            raise

    def recognize_attributes(self, image: np.ndarray) -> Dict:
        """Recognize attributes in image."""
        if not self.initialized:
            raise RuntimeError(VISION_PROCESSOR_NOT_INITIALIZED)

        try:
            # Preprocess image
            processed_image = self._preprocess_image(image)

            # Get attribute recognition predictions
            predictions = self.models["attribute_recognition"].predict(processed_image)

            # Apply quantum enhancement if enabled
            if self.config.use_quantum:
                predictions = self._apply_quantum_enhancements(predictions)

            # Process predictions
            return self._process_attribute_recognition(predictions)

        except Exception as e:
            self.logger.error(f"Failed to recognize attributes: {str(e)}")
            raise

    def _preprocess_image(self, image: np.ndarray) -> np.ndarray:
        """Preprocess image for model input."""
        # Resize image
        image = cv2.resize(
            image, (self.config.max_image_size, self.config.max_image_size)
        )

        # Normalize pixel values
        image = image.astype(np.float32) / 255.0

        # Add batch dimension
        image = np.expand_dims(image, axis=0)

        return image

    def _run_parallel_inference(self, image: np.ndarray) -> Dict:
        """Run parallel inference on all models."""
        tasks = [
            self.models["object_detection"].predict(image),
            self.models["scene_recognition"].predict(image),
            self.models["face_detection"].predict(image),
            self.models["attribute_recognition"].predict(image),
        ]

        results = asyncio.run(asyncio.gather(*tasks))

        return {
            "object_detection": results[0],
            "scene_recognition": results[1],
            "face_detection": results[2],
            "attribute_recognition": results[3],
        }

    def _post_process_results(self, results: Dict) -> Dict:
        """Post-process all results."""
        return {
            "objects": self._process_object_detections(results["object_detection"]),
            "scene": self._process_scene_analysis(results["scene_recognition"]),
            "faces": self._process_face_detections(results["face_detection"]),
            "attributes": self._process_attribute_recognition(
                results["attribute_recognition"]
            ),
        }

    def _process_object_detections(self, predictions: np.ndarray) -> List[Dict]:
        """Process object detection predictions."""
        # Extract predictions
        boxes = predictions[..., :4]
        scores = predictions[..., 4]
        classes = predictions[..., 5]

        # Filter by confidence
        mask = scores > self.config.confidence_threshold
        boxes = boxes[mask]
        scores = scores[mask]
        classes = classes[mask]

        # Convert to list of detections
        detections = []
        for box, score, class_id in zip(boxes, scores, classes):
            detections.append(
                {
                    "bbox": box.tolist(),
                    "confidence": float(score),
                    "class_id": int(class_id),
                    "class_name": self._get_class_name(int(class_id)),
                }
            )

        return detections

    def _process_scene_analysis(self, predictions: np.ndarray) -> Dict:
        """Process scene analysis predictions."""
        # Get top scene categories
        top_indices = np.argsort(predictions[0])[-5:][::-1]

        scenes = []
        for idx in top_indices:
            scenes.append(
                {
                    "category": self._get_scene_category(idx),
                    "confidence": float(predictions[0, idx]),
                }
            )

        return {"scenes": scenes, "primary_scene": scenes[0]}

    def _process_face_detections(self, predictions: np.ndarray) -> List[Dict]:
        """Process face detection predictions."""
        # Extract face detections
        boxes = predictions[..., :4]
        landmarks = predictions[..., 4:14]
        attributes = predictions[..., 14:]

        faces = []
        for box, face_landmarks, face_attributes in zip(boxes, landmarks, attributes):
            faces.append(
                {
                    "bbox": box.tolist(),
                    "landmarks": {
                        "left_eye": face_landmarks[:2].tolist(),
                        "right_eye": face_landmarks[2:4].tolist(),
                        "nose": face_landmarks[4:6].tolist(),
                        "mouth_left": face_landmarks[6:8].tolist(),
                        "mouth_right": face_landmarks[8:10].tolist(),
                    },
                    "attributes": {
                        "age": float(face_attributes[0]),
                        "gender": "male" if face_attributes[1] > 0.5 else "female",
                        "emotion": self._get_emotion(face_attributes[2:]),
                    },
                }
            )

        return faces

    def _process_attribute_recognition(self, predictions: np.ndarray) -> Dict:
        """Process attribute recognition predictions."""
        # Extract attribute predictions
        lighting = predictions[..., :4]
        weather = predictions[..., 4:8]
        time_of_day = predictions[..., 8:12]
        season = predictions[..., 12:16]

        return {
            "lighting": self._get_lighting(np.argmax(lighting[0])),
            "weather": self._get_weather(np.argmax(weather[0])),
            "time_of_day": self._get_time_of_day(np.argmax(time_of_day[0])),
            "season": self._get_season(np.argmax(season[0])),
        }

    def _apply_quantum_enhancements(self, features: np.ndarray) -> np.ndarray:
        """Apply quantum enhancements to features."""
        # Implement quantum feature enhancement
        quantum_features = self._quantum_processor.process_features(features)
        return quantum_features

    def _apply_advanced_processing(self, image: np.ndarray) -> np.ndarray:
        """Apply advanced image processing techniques."""
        # Implement advanced image processing
        processed_image = self._apply_enhancement(image)
        processed_image = self._apply_noise_reduction(processed_image)
        return processed_image

    async def _load_model(self) -> tf.keras.Model:
        """Load the vision model."""
        model_path = Path(self.config.model_path) / "vision_model"
        return tf.keras.models.load_model(str(model_path))

    def _get_class_name(self, class_id: int) -> str:
        """Get class name from ID."""
        # TODO: Implement class name mapping
        return f"class_{class_id}"

    def _get_scene_category(self, category_id: int) -> str:
        """Get scene category from ID."""
        # TODO: Implement scene category mapping
        return f"scene_{category_id}"

    def _get_emotion(self, emotion_scores: np.ndarray) -> str:
        """Get emotion from scores."""
        emotions = [
            "happy",
            "sad",
            "angry",
            "neutral",
            "surprised",
            "fearful",
            "disgusted",
        ]
        return emotions[np.argmax(emotion_scores)]

    def _get_lighting(self, lighting_id: int) -> str:
        """Get lighting condition from ID."""
        lighting_conditions = ["bright", "dim", "natural", "artificial"]
        return lighting_conditions[lighting_id]

    def _get_weather(self, weather_id: int) -> str:
        """Get weather condition from ID."""
        weather_conditions = ["sunny", "cloudy", "rainy", "snowy"]
        return weather_conditions[weather_id]

    def _get_time_of_day(self, time_id: int) -> str:
        """Get time of day from ID."""
        times = ["dawn", "day", "dusk", "night"]
        return times[time_id]

    def _get_season(self, season_id: int) -> str:
        """Get season from ID."""
        seasons = ["spring", "summer", "fall", "winter"]
        return seasons[season_id]
