dataset.py

import os
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms

# Dataset class
class CardiacMRIDataset(Dataset):
    def __init__(self, image_dir, transform=None):
        self.image_dir = image_dir
        self.transform = transform
        self.images = [file for file in os.listdir(image_dir) 
                      if file.endswith(('.png', '.jpg', '.jpeg', '.tif', '.bmp', '.dcm'))]
        
    def __len__(self):
        return len(self.images)
    
    def __getitem__(self, idx):
        img_path = os.path.join(self.image_dir, self.images[idx])
        image = Image.open(img_path).convert('L')  # Convert to grayscale
        
        if self.transform:
            image = self.transform(image)
            
        return {"images": image}

# Image preprocessing
def get_transforms(config, train=True):
    if train:
        return transforms.Compose([
            transforms.Resize((config.image_size, config.image_size)),
            transforms.RandomHorizontalFlip(p=0.5),
            transforms.RandomRotation(10),
            transforms.RandomAffine(degrees=0, translate=(0.05, 0.05)),
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5]),
        ])
    else:
        return transforms.Compose([
            transforms.Resize((config.image_size, config.image_size)),
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5]),
        ])