Security_AI_integrated/config.py

import os
from pathlib import Path
from typing import Any, Dict, List, Optional

import yaml
from pydantic import BaseModel, Field


class DatabaseConfig(BaseModel):
    dialect: str = "sqlite"
    host: str = "localhost"
    port: int = 3306
    username: str = "root"
    password: str = "password"
    name: str = "security_monitor"
    echo: bool = False

    @property
    def url(self) -> str:
        if self.dialect == "sqlite":
            return f"sqlite:///{self.name}.db"
        return f"mysql+pymysql://{self.username}:{self.password}@{self.host}:{self.port}/{self.name}"


class ModelConfig(BaseModel):
    engine_path: str = "models/yolo11n_fp16_480.engine"
    pt_model_path: str = "models/yolo11n.pt"
    imgsz: List[int] = [480, 480]
    conf_threshold: float = 0.5
    iou_threshold: float = 0.45
    device: int = 0
    batch_size: int = 8
    half: bool = True


class StreamConfig(BaseModel):
    buffer_size: int = 2
    reconnect_delay: float = 3.0
    timeout: float = 10.0
    fps_limit: int = 30


class InferenceConfig(BaseModel):
    queue_maxlen: int = 100
    event_queue_maxlen: int = 1000
    worker_threads: int = 4


class AlertConfig(BaseModel):
    snapshot_path: str = "data/alerts"
    cooldown_sec: int = 300
    image_quality: int = 85


class ROIConfig(BaseModel):
    default_types: List[str] = ["polygon", "line"]
    max_points: int = 50


class WorkingHours(BaseModel):
    start: List[int] = Field(default_factory=lambda: [8, 30])
    end: List[int] = Field(default_factory=lambda: [17, 30])


class AlgorithmsConfig(BaseModel):
    leave_post_threshold_sec: int = 360
    leave_post_confirm_sec: int = 30
    leave_post_return_sec: int = 5
    intrusion_check_interval_sec: float = 1.0
    intrusion_direction_sensitive: bool = False


class LoggingConfig(BaseModel):
    level: str = "INFO"
    format: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
    file: str = "logs/app.log"
    max_size: str = "100MB"
    backup_count: int = 5


class MonitoringConfig(BaseModel):
    enabled: bool = True
    port: int = 9090
    path: str = "/metrics"


class LLMConfig(BaseModel):
    enabled: bool = False
    api_key: str = ""
    base_url: str = ""
    model: str = "qwen-vl-max"
    timeout: int = 30


class Config(BaseModel):
    database: DatabaseConfig = Field(default_factory=DatabaseConfig)
    model: ModelConfig = Field(default_factory=ModelConfig)
    stream: StreamConfig = Field(default_factory=StreamConfig)
    inference: InferenceConfig = Field(default_factory=InferenceConfig)
    alert: AlertConfig = Field(default_factory=AlertConfig)
    roi: ROIConfig = Field(default_factory=ROIConfig)
    working_hours: List[WorkingHours] = Field(default_factory=lambda: [
        WorkingHours(start=[8, 30], end=[11, 0]),
        WorkingHours(start=[12, 0], end=[17, 30])
    ])
    algorithms: AlgorithmsConfig = Field(default_factory=AlgorithmsConfig)
    logging: LoggingConfig = Field(default_factory=LoggingConfig)
    monitoring: MonitoringConfig = Field(default_factory=MonitoringConfig)
    llm: LLMConfig = Field(default_factory=LLMConfig)


_config: Optional[Config] = None


def load_config(config_path: Optional[str] = None) -> Config:
    """加载配置文件"""
    global _config

    if _config is not None:
        return _config

    if config_path is None:
        config_path = os.environ.get(
            "CONFIG_PATH",
            str(Path(__file__).parent / "config.yaml")
        )

    if os.path.exists(config_path):
        with open(config_path, "r", encoding="utf-8") as f:
            yaml_config = yaml.safe_load(f) or {}
    else:
        yaml_config = {}

    _config = Config(**yaml_config)
    return _config


def get_config() -> Config:
    """获取配置单例"""
    if _config is None:
        return load_config()
    return _config


def reload_config(config_path: Optional[str] = None) -> Config:
    """重新加载配置"""
    global _config
    _config = None
    return load_config(config_path)