"""UltralyticsDetector —— ultralytics YOLO .pt 直载的检测后端。

⚠ 移植注记（C++ 侧需自行复现，详见 README）：letterbox（缩放 + 填充到
imgsz）、输出张量解码、NMS、框映射回输入图原生坐标系，本例由 ultralytics
库内完成；移植时必须逐项复现同一几何（步长对齐、填充方式、IoU 门限、
原生坐标回映），否则视差取点会引入系统性偏差。
"""

import queue
from pathlib import Path
from typing import List

from stereo_ranger.types import Detection

from .config import StereoYoloConfig


class UltralyticsDetector:
    """双实例池：ultralytics 单实例非线程安全，左右并发各持一份（§3/§4）。

    接口约定（可注入替换）：``detect(image) -> list[Detection]``，
    image 为 BGR ndarray（cv2 约定），返回框位于该图原生坐标系。
    """

    def __init__(self, config: StereoYoloConfig) -> None:
        self._cfg = config
        self._model_path = _resolve_model_path(config.model_path)
        self._pool: "queue.SimpleQueue" = queue.SimpleQueue()
        from ultralytics import YOLO  # 延迟导入：仅真实推理付加载成本，单测注入假检测器（§6）

        for _ in range(2):
            self._pool.put(YOLO(str(self._model_path)))

    def detect(self, image) -> List[Detection]:
        model = self._pool.get()
        try:
            results = model.predict(
                source=image,
                imgsz=self._cfg.imgsz,
                conf=self._cfg.conf_threshold,
                iou=self._cfg.iou_threshold,
                device=self._cfg.device,
                verbose=False,
            )
        finally:
            self._pool.put(model)
        if not results:
            return []
        return _result_to_detections(results[0])


def _result_to_detections(result) -> List[Detection]:
    boxes = getattr(result, "boxes", None)
    if boxes is None or len(boxes) == 0:
        return []
    xyxy = boxes.xyxy.cpu().numpy()
    conf = boxes.conf.cpu().numpy()
    cls = boxes.cls.cpu().numpy()
    return [
        Detection(
            float(box[0]),
            float(box[1]),
            float(box[2]),
            float(box[3]),
            confidence=float(score),
            class_id=int(class_id),
        )
        for box, score, class_id in zip(xyxy, conf, cls)
    ]


def _resolve_model_path(raw: str) -> Path:
    path = Path(raw).expanduser()
    if not path.is_absolute():
        path = Path.cwd() / path
    if not path.is_file():
        raise FileNotFoundError(f"模型权重不存在: {path}（model_path 相对路径按启动工作目录解析）")
    return path.resolve()