import os
from dataclasses import dataclass


def _get_str(name: str, default: str) -> str:
    return os.getenv(name, default)


def _get_int(name: str, default: int) -> int:
    value = os.getenv(name)
    if value is None or value == "":
        return default
    return int(value)


def _get_float(name: str, default: float) -> float:
    value = os.getenv(name)
    if value is None or value == "":
        return default
    return float(value)


def _get_bool(name: str, default: bool) -> bool:
    value = os.getenv(name)
    if value is None or value == "":
        return default
    return value.lower() in ["1", "true", "yes", "y"]


def _get_int_list(name: str, default: str) -> list[int]:
    value = os.getenv(name, default)
    if value is None or value.strip() == "":
        return []
    return [int(x.strip()) for x in value.split(",") if x.strip() != ""]


@dataclass(frozen=True)
class Settings:
    # Docker 内默认模型路径
    det_model_path: str = _get_str(
        "DET_MODEL_PATH",
        "/app/models/YNDC_20251010_yolov5_optimize.onnx",
    )
    cls_model_path: str = _get_str(
        "CLS_MODEL_PATH",
        "/app/models/HHK_20260525_mmcls_optimize.onnx",
    )
    det_classes_path: str = _get_str(
        "DET_CLASSES_PATH",
        "/app/models/YNDC_20251010_yolov5_optimize.txt",
    )
    cls_classes_path: str = _get_str(
        "CLS_CLASSES_PATH",
        "/app/models/HHK_20260525_mmcls_optimize.txt",
    )

    # 输入尺寸
    det_img_size: int = _get_int("DET_IMG_SIZE", 640)
    cls_img_size: int = _get_int("CLS_IMG_SIZE", 224)

    # YOLO 检测阈值
    det_conf_thres: float = _get_float("DET_CONF_THRES", 0.25)
    det_iou_thres: float = _get_float("DET_IOU_THRES", 0.45)

    # 目标裁剪外扩比例
    crop_padding_ratio: float = _get_float("CROP_PADDING_RATIO", 0.08)

    # 最多保留目标数量
    max_detections: int = _get_int("MAX_DETECTIONS", 50)

    # 当前环境没有 onnxruntime-gpu 时，建议用 cpu
    device: str = _get_str("DEVICE", "cpu")

    # 输出目录
    output_dir: str = _get_str("OUTPUT_DIR", "/app/runs")
    image_output_subdir: str = _get_str("IMAGE_OUTPUT_SUBDIR", "images")
    video_output_subdir: str = _get_str("VIDEO_OUTPUT_SUBDIR", "video")

    # 视频抽帧间隔
    video_sample_interval: int = _get_int("VIDEO_SAMPLE_INTERVAL", 2)

    # 是否保存画框后视频
    save_annotated_video: bool = _get_bool("SAVE_ANNOTATED_VIDEO", True)

    # 只保留 YOLO 类别 0，也就是“鸟”
    target_det_class_ids: list[int] = None

    # 视频平滑参数
    smooth_video_boxes: bool = _get_bool("SMOOTH_VIDEO_BOXES", True)

    # 未处理帧或短暂漏检时，最多复用多少帧上一结果
    smooth_hold_frames: int = _get_int("SMOOTH_HOLD_FRAMES", 3)

    # 框坐标平滑系数，越大越稳定，但响应越慢
    box_smooth_alpha: float = _get_float("BOX_SMOOTH_ALPHA", 0.25)

    def __post_init__(self):
        object.__setattr__(
            self,
            "target_det_class_ids",
            _get_int_list("TARGET_DET_CLASS_IDS", "0"),
        )


settings = Settings()