core_dataset.py

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

class CoreDataset(Dataset):
    def __init__(self, image_dir, transform=None):
        self.image_dir = image_dir
        self.image_paths = [
            os.path.join(image_dir, f)
            for f in os.listdir(image_dir)
            if f.lower().endswith(('.png', '.jpg', '.jpeg'))
        ]
        self.transform = transform or self.default_transform()

    def default_transform(self):
        return transforms.Compose([
            transforms.Resize(IMAGE_SIZE),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                               std=[0.229, 0.224, 0.225])
        ])

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

    def __getitem__(self, idx):
        img_path = self.image_paths[idx]
        image = Image.open(img_path).convert("RGB")
        if self.transform:
            image = self.transform(image)
        return image, img_path