def load_dataset_images(directory):
    import pickle
    import os
    import cv2
    import numpy as np

    # Set the paths to the dataset folders
    dataset_dir = "general"
    train_dir = os.path.join(dataset_dir, "PANTOLON")

    # Set the image size to be used as the input data for the network
    image_size = (224, 224)

    preprocessed_images_file = os.path.join(directory, 'preprocessed_images.pkl')
    labels_file = os.path.join(directory, 'labels.pkl')
    filenames_file = os.path.join(directory, 'filenames.pkl')

    # Check if preprocessed images, labels and filenames already exist
    if os.path.exists(preprocessed_images_file) and os.path.exists(labels_file) and os.path.exists(filenames_file):
        # Load preprocessed images, labels and filenames from disk
        with open(preprocessed_images_file, 'rb') as f:
            images = pickle.load(f)
        with open(labels_file, 'rb') as f:
            labels = pickle.load(f)
        with open(filenames_file, 'rb') as f:
            filenames = pickle.load(f)
    else:
        # Preprocess images, labels and filenames and save them to disk
        images = []
        labels = []
        filenames = []

        for filename in os.listdir(directory):
            if filename.endswith(".txt"):
                with open(os.path.join(directory, filename), "r", encoding="utf-8") as file:
                    product = file.readline().strip().replace("Product: ", "")
                    labels.append(product)

            if filename.endswith(".jpg") or filename.endswith(".JPG") or filename.endswith(
                    ".jpeg") or filename.endswith(".png"):
                img_path = os.path.join(directory, filename)
                img = cv2.imread(img_path)
                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
                img = cv2.resize(img, image_size)
                img = img.astype("float32") / 255.0  # Normalize the pixel values
                images.append(img)
                filenames.append(filename)

        images = np.array(images)

        # Save preprocessed images, labels and filenames to disk
        with open(preprocessed_images_file, 'wb') as f:
            pickle.dump(images, f)
        with open(labels_file, 'wb') as f:
            pickle.dump(labels, f)
        with open(filenames_file, 'wb') as f:
            pickle.dump(filenames, f)

    return images, labels, filenames