# 檔案路徑: app/services/reference_detection_service.py

import logging
import numpy as np
from PIL import Image
from typing import Dict, Any, List, Optional, Tuple
from ultralytics import YOLO
import io

# 設置日誌
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# 參考物尺寸表 (cm)
REFERENCE_OBJECTS = {
    "plate": {"diameter": 24.0, "type": "circular"},      # 標準餐盤直徑
    "bowl": {"diameter": 15.0, "type": "circular"},       # 標準碗直徑
    "spoon": {"length": 15.0, "type": "linear"},          # 湯匙長度
    "fork": {"length": 20.0, "type": "linear"},           # 叉子長度
    "knife": {"length": 20.0, "type": "linear"},          # 刀子長度
    "coin": {"diameter": 2.4, "type": "circular"},        # 硬幣直徑
    "credit_card": {"length": 8.5, "width": 5.4, "type": "rectangular"},  # 信用卡
    "default": {"diameter": 24.0, "type": "circular"}     # 預設參考物
}

class ReferenceDetectionService:
    def __init__(self):
        """初始化參考物偵測服務"""
        self.yolo_model = None
        self._load_model()
    
    def _load_model(self):
        """載入 YOLO 模型"""
        try:
            logger.info("正在載入 YOLO 參考物偵測模型...")
            # 使用 YOLOv8n 作為基礎模型
            self.yolo_model = YOLO("yolov8n.pt")
            logger.info("YOLO 模型載入完成！")
            
        except Exception as e:
            logger.error(f"YOLO 模型載入失敗: {str(e)}")
            raise
    
    def detect_reference_objects(self, image: Image.Image) -> List[Dict[str, Any]]:
        """
        使用 YOLO 偵測圖片中的參考物
        
        Args:
            image: PIL Image 物件
            
        Returns:
            List[Dict]: 包含參考物資訊的列表
        """
        try:
            results = self.yolo_model(image)
            reference_objects = []
            
            for result in results[0].boxes.data.tolist():
                x1, y1, x2, y2, conf, class_id = result
                label = self.yolo_model.model.names[int(class_id)].lower()
                
                # 只關注參考物類別
                if self._is_reference_object(label) and conf > 0.3:
                    reference_objects.append({
                        "label": label,
                        "bbox": [x1, y1, x2, y2],
                        "confidence": conf,
                        "area": (x2 - x1) * (y2 - y1),  # 像素面積
                        "dimensions": self._get_reference_dimensions(label)
                    })
            
            # 按信心度排序，優先選擇高信心度的參考物
            reference_objects.sort(key=lambda x: x["confidence"], reverse=True)
            
            logger.info(f"偵測到 {len(reference_objects)} 個參考物: {[obj['label'] for obj in reference_objects]}")
            return reference_objects
            
        except Exception as e:
            logger.error(f"參考物偵測失敗: {str(e)}")
            return []
    
    def _is_reference_object(self, label: str) -> bool:
        """判斷是否為參考物"""
        reference_labels = [
            "plate", "bowl", "spoon", "fork", "knife", 
            "coin", "credit card", "card", "phone", "remote"
        ]
        return any(ref_label in label for ref_label in reference_labels)
    
    def _get_reference_dimensions(self, label: str) -> Dict[str, Any]:
        """取得參考物的實際尺寸"""
        for ref_name, dimensions in REFERENCE_OBJECTS.items():
            if ref_name in label:
                return dimensions
        return REFERENCE_OBJECTS["default"]
    
    def calculate_pixel_ratio(self, reference_object: Dict[str, Any]) -> float:
        """
        根據參考物計算像素到實際距離的比例
        
        Args:
            reference_object: 參考物資訊
            
        Returns:
            float: 像素比例 (cm/pixel)
        """
        try:
            bbox = reference_object["bbox"]
            dimensions = reference_object["dimensions"]
            
            # 計算參考物在圖片中的像素尺寸
            pixel_width = bbox[2] - bbox[0]
            pixel_height = bbox[3] - bbox[1]
            
            if dimensions["type"] == "circular":
                # 圓形參考物（如餐盤、碗、硬幣）
                pixel_diameter = min(pixel_width, pixel_height)  # 取較小值作為直徑
                actual_diameter = dimensions["diameter"]
                pixel_ratio = actual_diameter / pixel_diameter
                
            elif dimensions["type"] == "linear":
                # 線性參考物（如餐具）
                pixel_length = max(pixel_width, pixel_height)  # 取較大值作為長度
                actual_length = dimensions["length"]
                pixel_ratio = actual_length / pixel_length
                
            elif dimensions["type"] == "rectangular":
                # 矩形參考物（如信用卡）
                pixel_length = max(pixel_width, pixel_height)
                actual_length = dimensions["length"]
                pixel_ratio = actual_length / pixel_length
                
            else:
                # 預設情況
                pixel_ratio = 0.01  # 100像素 = 1cm
            
            logger.info(f"參考物 {reference_object['label']} 像素比例: {pixel_ratio:.4f} cm/pixel")
            return pixel_ratio
            
        except Exception as e:
            logger.error(f"計算像素比例失敗: {str(e)}")
            return 0.01  # 預設值
    
    def get_best_reference_object(self, reference_objects: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
        """
        從偵測到的參考物中選擇最佳的參考物
        
        Args:
            reference_objects: 參考物列表
            
        Returns:
            Optional[Dict]: 最佳參考物，如果沒有則返回 None
        """
        if not reference_objects:
            return None
        
        # 優先選擇餐盤或碗，因為它們通常最穩定
        priority_objects = ["plate", "bowl"]
        
        for obj in reference_objects:
            if any(priority in obj["label"] for priority in priority_objects):
                return obj
        
        # 如果沒有優先參考物，選擇信心度最高的
        return reference_objects[0]

# 全域服務實例
reference_service = ReferenceDetectionService()

def detect_reference_objects_from_image(image_bytes: bytes) -> Tuple[List[Dict[str, Any]], Optional[float]]:
    """
    從圖片中偵測參考物並計算像素比例
    
    Args:
        image_bytes: 圖片二進位數據
        
    Returns:
        Tuple[List[Dict], Optional[float]]: (參考物列表, 像素比例)
    """
    try:
        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        
        # 偵測參考物
        reference_objects = reference_service.detect_reference_objects(image)
        
        # 選擇最佳參考物
        best_reference = reference_service.get_best_reference_object(reference_objects)
        
        # 計算像素比例
        pixel_ratio = None
        if best_reference:
            pixel_ratio = reference_service.calculate_pixel_ratio(best_reference)
        
        return reference_objects, pixel_ratio
        
    except Exception as e:
        logger.error(f"參考物偵測失敗: {str(e)}")
        return [], None