Datasets:
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License:
| import base64 | |
| from PIL import Image | |
| import io | |
| import os | |
| import pandas as pd | |
| from datasets import load_dataset | |
| def decode_and_save_images(df, output_dir): | |
| for i, (image_base64, caption) in enumerate(zip(df['image'], df['caption'])): | |
| # Decode and save the image | |
| image_data = base64.b64decode(image_base64) | |
| image = Image.open(io.BytesIO(image_data)) | |
| image.save(os.path.join(output_dir, f"image_{i}.png")) | |
| # Save the caption | |
| with open(os.path.join(output_dir, f"caption_{i}.txt"), 'w') as file: | |
| file.write(caption) | |
| print(f"Saved Image and Caption {i}") | |
| def main(): | |
| # Load dataset from Hugging Face | |
| dataset = load_dataset("dataautogpt3/Dalle3") | |
| # Assuming the first split contains the data | |
| df = pd.DataFrame(dataset[next(iter(dataset))]) | |
| # Specify your desired output directory here | |
| output_dir = '/path/to/your/desired/output' # Replace with your specific path | |
| os.makedirs(output_dir, exist_ok=True) | |
| # Process and save images and captions | |
| decode_and_save_images(df, output_dir) | |
| if __name__ == "__main__": | |
| main() | |