data/dataset.py

from pathlib import Path
import cv2
import numpy as np
import torch
from torch.utils.data import Dataset


class CrackSegDataset(Dataset):
    def __init__(self, images_dir: str, masks_dir: str, transform=None):
        self.images_dir = Path(images_dir)
        self.masks_dir = Path(masks_dir)
        self.transform = transform
        self.image_paths = sorted([p for p in self.images_dir.glob("*") if p.suffix.lower() in {".jpg",".jpeg",".png"}])

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        img_path = self.image_paths[idx]
        mask_path = self.masks_dir / (img_path.stem + ".png")
        if not mask_path.exists():
            raise FileNotFoundError(f"Mask not found for {img_path.name}: {mask_path}")

        image = cv2.imread(str(img_path), cv2.IMREAD_COLOR)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE)
        mask = (mask > 127).astype(np.uint8)  # binarize

        if self.transform is not None:
            augmented = self.transform(image=image, mask=mask)
            image, mask = augmented["image"], augmented["mask"]

        # albumentations returns HWC image; convert to CHW float tensor
        image = torch.from_numpy(image).permute(2, 0, 1).float() / 255.0
        mask = torch.from_numpy(mask).unsqueeze(0).float()  # [1,H,W]

        return image, mask