backend/app/modules/input_preprocess.py

"""OpenCV-based preprocessing for uploaded room photos.

Validates decodability, applies EXIF orientation, resizes so the longest edge is
<= MAX_EDGE_PX (aspect preserved), and writes a normalized copy to disk.
"""
from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path

import cv2
import numpy as np
from PIL import Image, ImageOps

from ..config import settings


@dataclass
class PreprocessResult:
    path: Path        # normalized image path on disk
    rel_path: str     # path relative to the server root (e.g. "static/uploads/x.jpg")
    width: int
    height: int


def _load_oriented(raw_path: Path) -> Image.Image:
    img = Image.open(raw_path)
    img = ImageOps.exif_transpose(img)  # honor EXIF orientation
    return img.convert("RGB")


def preprocess_image(
    raw_path: Path, out_path: Path, max_edge: int | None = None
) -> PreprocessResult:
    """Validate, auto-orient, resize and save a normalized image.

    Raises ValueError if the file cannot be decoded as an image.
    """
    max_edge = max_edge or settings.MAX_EDGE_PX
    try:
        pil = _load_oriented(raw_path)
    except Exception as exc:  # noqa: BLE001
        raise ValueError(f"Could not decode image: {raw_path.name}") from exc

    arr = cv2.cvtColor(np.array(pil), cv2.COLOR_RGB2BGR)
    if arr is None or arr.size == 0:
        raise ValueError(f"Empty/invalid image: {raw_path.name}")

    h, w = arr.shape[:2]
    scale = min(1.0, max_edge / float(max(h, w)))
    if scale < 1.0:
        w, h = int(round(w * scale)), int(round(h * scale))
        arr = cv2.resize(arr, (w, h), interpolation=cv2.INTER_AREA)

    out_path.parent.mkdir(parents=True, exist_ok=True)
    cv2.imwrite(str(out_path), arr)

    rel = out_path.relative_to(settings.STATIC_DIR).as_posix()
    return PreprocessResult(path=out_path, rel_path=f"static/{rel}", width=w, height=h)