load_datasets.py

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


class DatasetLoader(Dataset):
    def __init__(self, csv_path):
        self.csv_file = csv_path
        with open(self.csv_file, 'r') as file:
            self.data = list(csv.reader(file))

        self.current_dir = os.path.dirname(os.path.abspath(__file__))

    def preprocess_image(self, image_path):
        """
        Preprocess the image: Read the image, apply transformations, and return the transformed image.
        """
        full_path = os.path.join(self.current_dir, 'datasets', image_path)
        image = Image.open(full_path)
        image_transform = transforms.Compose([
            transforms.Resize((256, 256)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])

        return image_transform(image)

    def __getitem__(self, index):
        """
        Return the preprocessed image and its label at the specified index from the dataset.
        """
        image_path, label = self.data[index]
        image = self.preprocess_image(image_path)
        return image, int(label)

    def __len__(self):
        """
        Return the number of items in the dataset.
        """
        return len(self.data)