| from __future__ import annotations | |
| from typing import Tuple | |
| import torch | |
| from PIL import Image | |
| from torchvision import transforms | |
| from app.model import MODEL_INPUT_SIZE | |
| NORMALIZE_MEAN: Tuple[float, float, float] = (0.5, 0.5, 0.5) | |
| NORMALIZE_STD: Tuple[float, float, float] = (0.5, 0.5, 0.5) | |
| def build_preprocess() -> transforms.Compose: | |
| """Build the notebook-matching preprocessing pipeline.""" | |
| return transforms.Compose( | |
| [ | |
| transforms.Resize((MODEL_INPUT_SIZE, MODEL_INPUT_SIZE)), | |
| transforms.ToTensor(), | |
| transforms.Normalize(NORMALIZE_MEAN, NORMALIZE_STD), | |
| ] | |
| ) | |
| def preprocess_image(image: Image.Image) -> torch.Tensor: | |
| """Convert PIL image to normalized batch tensor [1, 3, 224, 224].""" | |
| transform = build_preprocess() | |
| return transform(image).unsqueeze(0) | |