experiments/resolution_study/dataset.py

"""
SolarPanelDataset for the resolution study. Identical to the clean baseline's
dataset apart from taking image_size as an argument (which the trainer varies).
"""
from pathlib import Path
import torch
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms


class SolarPanelDataset(Dataset):
    def __init__(self, image_dir, mask_dir, image_size=128, augment=False):
        self.image_dir = Path(image_dir)
        self.mask_dir = Path(mask_dir)
        self.image_size = image_size
        self.augment = augment

        self.image_files = sorted(p.name for p in self.image_dir.iterdir() if p.suffix == ".jpg")

        self.image_transform = transforms.Compose([
            transforms.Resize((image_size, image_size)),
            transforms.ToTensor(),
        ])
        self.mask_transform = transforms.Compose([
            transforms.Resize((image_size, image_size)),
            transforms.ToTensor(),
        ])
        self.augment_transform = transforms.Compose([
            transforms.RandomHorizontalFlip(p=0.5),
            transforms.RandomVerticalFlip(p=0.5),
            transforms.RandomRotation(15),
        ])

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

    def __getitem__(self, idx):
        img_name = self.image_files[idx]
        image = Image.open(self.image_dir / img_name).convert("RGB")
        mask = Image.open(self.mask_dir / img_name.replace(".jpg", "_mask.png")).convert("L")

        image = self.image_transform(image)
        mask = self.mask_transform(mask)

        if self.augment:
            seed = torch.randint(0, 2**32, (1,)).item()
            torch.manual_seed(seed)
            image = self.augment_transform(image)
            torch.manual_seed(seed)
            mask = self.augment_transform(mask)

        mask = (mask > 0.5).float()
        return image, mask