"""医学影像预处理：优先 MONAI，失败则用 OpenCV/Pillow 降级。"""
from __future__ import annotations

import logging
from typing import Any

import cv2
import numpy as np

logger = logging.getLogger(__name__)

_MONAI_OK = False
try:
    import monai  # noqa: F401
    from monai.transforms import Compose, ScaleIntensity, Resize

    _MONAI_OK = True
except Exception:  # pragma: no cover
    _MONAI_OK = False
    logger.info("MONAI 未安装，使用 OpenCV 预处理管线")


def monai_available() -> bool:
    return _MONAI_OK


def load_image_bgr(image_bytes: bytes) -> np.ndarray:
    arr = np.frombuffer(image_bytes, dtype=np.uint8)
    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
    if img is None:
        raise ValueError("无法解码影像文件，请上传常见图片格式（jpg/png 等）")
    return img


def preprocess(image_bgr: np.ndarray, target_size: int = 640) -> dict[str, Any]:
    """
    返回：
      - image_bgr: 原始 BGR
      - image_rgb: RGB
      - tensor_like: 归一化后的 float32 CHW（MONAI 或 numpy 模拟）
      - meta: 尺寸信息
    """
    h, w = image_bgr.shape[:2]
    rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)

    if _MONAI_OK:
        try:
            # 灰度/三通道统一为 CHW float，再经 MONAI ScaleIntensity + Resize
            chw = np.transpose(rgb.astype(np.float32) / 255.0, (2, 0, 1))
            transforms = Compose(
                [
                    ScaleIntensity(minv=0.0, maxv=1.0),
                    Resize(spatial_size=(target_size, target_size), mode="bilinear"),
                ]
            )
            tensor = transforms(chw)
            if hasattr(tensor, "numpy"):
                tensor = tensor.numpy()
            return {
                "image_bgr": image_bgr,
                "image_rgb": rgb,
                "tensor_like": np.asarray(tensor),
                "backend": "monai",
                "meta": {"orig_h": h, "orig_w": w, "target": target_size},
            }
        except Exception as e:  # pragma: no cover
            logger.warning("MONAI 预处理失败，降级 OpenCV: %s", e)

    # OpenCV 降级：resize + normalize
    resized = cv2.resize(rgb, (target_size, target_size), interpolation=cv2.INTER_LINEAR)
    tensor = np.transpose(resized.astype(np.float32) / 255.0, (2, 0, 1))
    return {
        "image_bgr": image_bgr,
        "image_rgb": rgb,
        "tensor_like": tensor,
        "backend": "opencv",
        "meta": {"orig_h": h, "orig_w": w, "target": target_size},
    }