from http.client import HTTPException

import numpy as np
import torch
from MySQLdb import MySQLError
from ultralytics import YOLO
import os

from ds.db import db
from service.file_service import get_absolute_path

# 全局变量
current_yolo_model = None
current_model_absolute_path = None  # 存储模型绝对路径，不依赖model实例

ALLOWED_MODEL_EXT = {"pt"}
MAX_MODEL_SIZE = 100 * 1024 * 1024  # 100MB


def load_yolo_model():
    """加载模型并存储绝对路径"""
    global current_yolo_model, current_model_absolute_path
    model_rel_path = get_enabled_model_rel_path()
    print(f"[模型初始化] 加载模型：{model_rel_path}")

    # 计算并存储绝对路径
    current_model_absolute_path = get_absolute_path(model_rel_path)
    print(f"[模型初始化] 绝对路径：{current_model_absolute_path}")

    # 检查模型文件
    if not os.path.exists(current_model_absolute_path):
        raise FileNotFoundError(f"模型文件不存在: {current_model_absolute_path}")

    try:
        new_model = YOLO(current_model_absolute_path)
        if torch.cuda.is_available():
            new_model.to('cuda')
            print("模型已移动到GPU")
        else:
            print("使用CPU进行推理")
        current_yolo_model = new_model
        print(f"成功加载模型: {current_model_absolute_path}")
        return current_yolo_model
    except Exception as e:
        print(f"模型加载失败：{str(e)}")
        raise


def get_current_model():
    """获取当前模型实例"""
    if current_yolo_model is None:
        raise ValueError("尚未加载任何YOLO模型，请先调用load_yolo_model加载模型")
    return current_yolo_model


def detect(image_np, conf_threshold=0.8):
    # 1. 输入格式验证
    if not isinstance(image_np, np.ndarray):
        raise ValueError("输入必须是numpy数组（BGR图像）")
    if image_np.ndim != 3 or image_np.shape[-1] != 3:
        raise ValueError(f"输入图像格式错误，需为 (h, w, 3) 的BGR数组，当前shape: {image_np.shape}")
    detection_results = []
    try:
        model = get_current_model()
        if not current_model_absolute_path:
            raise RuntimeError("模型未初始化！请先调用 load_yolo_model 加载模型")
        device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"检测设备：{device} | 置信度阈值：{conf_threshold}")

        # 图像尺寸信息
        img_height, img_width = image_np.shape[:2]
        print(f"输入图像尺寸：{img_width}x{img_height}")

        # YOLO检测
        print("执行YOLO检测")
        results = model.predict(
            image_np,
            conf=conf_threshold,
            device=device,
            show=False,
        )

        # 4. 整理检测结果（仅保留Chest类别，ID=2）
        for box in results[0].boxes:
            class_id = int(box.cls[0])  # 类别ID
            class_name = model.names[class_id]
            confidence = float(box.conf[0])
            bbox = tuple(map(int, box.xyxy[0]))

            # 过滤条件：置信度达标 + 类别为Chest（class_id=2）
            # and class_id == 2
            if confidence >= conf_threshold:
                detection_results.append({
                    "class": class_name,
                    "confidence": confidence,
                    "bbox": bbox
                })

        # 判断是否有目标
        has_content = len(detection_results) > 0
        return has_content, detection_results

    except Exception as e:
        error_msg = f"检测过程出错：{str(e)}"
        print(error_msg)
        return False, None


def get_enabled_model_rel_path():
    """获取数据库中启用的模型相对路径"""
    conn = None
    cursor = None
    try:
        conn = db.get_connection()
        cursor = conn.cursor(dictionary=True)
        query = "SELECT path FROM model WHERE is_default = 1 LIMIT 1"
        cursor.execute(query)
        result = cursor.fetchone()

        if not result or not result.get('path'):
            raise HTTPException(status_code=404, detail="未找到启用的默认模型")

        return result['path']
    except MySQLError as e:
        raise HTTPException(status_code=500, detail=f"查询默认模型时发生数据库错误：{str(e)}") from e
    except Exception as e:
        if isinstance(e, HTTPException):
            raise e
        raise HTTPException(status_code=500, detail=f"获取默认模型路径失败：{str(e)}") from e
    finally:
        db.close_connection(conn, cursor)