79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
"""医学影像预处理：优先 MONAI，失败则用 OpenCV/Pillow 降级。"""
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from __future__ import annotations
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import logging
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from typing import Any
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import cv2
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import numpy as np
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logger = logging.getLogger(__name__)
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_MONAI_OK = False
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try:
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import monai # noqa: F401
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from monai.transforms import Compose, ScaleIntensity, Resize
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_MONAI_OK = True
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except Exception: # pragma: no cover
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_MONAI_OK = False
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logger.info("MONAI 未安装，使用 OpenCV 预处理管线")
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def monai_available() -> bool:
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return _MONAI_OK
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def load_image_bgr(image_bytes: bytes) -> np.ndarray:
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arr = np.frombuffer(image_bytes, dtype=np.uint8)
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img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
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if img is None:
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raise ValueError("无法解码影像文件，请上传常见图片格式（jpg/png 等）")
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return img
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def preprocess(image_bgr: np.ndarray, target_size: int = 640) -> dict[str, Any]:
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"""
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返回：
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- image_bgr: 原始 BGR
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- image_rgb: RGB
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- tensor_like: 归一化后的 float32 CHW（MONAI 或 numpy 模拟）
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- meta: 尺寸信息
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"""
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h, w = image_bgr.shape[:2]
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rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
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if _MONAI_OK:
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try:
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# 灰度/三通道统一为 CHW float，再经 MONAI ScaleIntensity + Resize
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chw = np.transpose(rgb.astype(np.float32) / 255.0, (2, 0, 1))
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transforms = Compose(
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[
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ScaleIntensity(minv=0.0, maxv=1.0),
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Resize(spatial_size=(target_size, target_size), mode="bilinear"),
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]
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)
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tensor = transforms(chw)
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if hasattr(tensor, "numpy"):
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tensor = tensor.numpy()
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return {
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"image_bgr": image_bgr,
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"image_rgb": rgb,
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"tensor_like": np.asarray(tensor),
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"backend": "monai",
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"meta": {"orig_h": h, "orig_w": w, "target": target_size},
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}
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except Exception as e: # pragma: no cover
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logger.warning("MONAI 预处理失败，降级 OpenCV: %s", e)
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# OpenCV 降级：resize + normalize
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resized = cv2.resize(rgb, (target_size, target_size), interpolation=cv2.INTER_LINEAR)
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tensor = np.transpose(resized.astype(np.float32) / 255.0, (2, 0, 1))
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return {
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"image_bgr": image_bgr,
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"image_rgb": rgb,
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"tensor_like": tensor,
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"backend": "opencv",
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"meta": {"orig_h": h, "orig_w": w, "target": target_size},
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}
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