data_prepration_scripts/Retinal-Datasets/retina_webdataset_part1.py

import os
import tarfile
import io
import pandas as pd
import ast
from tqdm import tqdm
import argparse

banned_categories = ['myopia', 'cataract', 'macular hole', 'retinitis pigmentosa', "myopic", "myope", "myop", "retinitis"]

def create_webdataset(main_csv_directory, image_dir_path, output_dir, tar_size=1000):
    os.makedirs(output_dir, exist_ok=True)
    # Load both csv files
    all_datasets = os.listdir(main_csv_directory)
    tar_index = 0
    file_count = 0
    tar = None
    for iDataset in tqdm(all_datasets):
        print("Processing data: " + iDataset)
        if iDataset == "06_DEN.csv" or iDataset == "39_MM_Retinal_dataset.csv" or \
                iDataset == "28_OIA-DDR_revised.csv" or iDataset == '07_LAG_revised.csv' \
                or iDataset == '01_EYEPACS_revised.csv':
            continue
        dataframe = pd.read_csv(main_csv_directory + iDataset)
        selected_id_list = range(len(dataframe))  # 100%数据   100% data

        for i in selected_id_list:
            if file_count % tar_size == 0:
                if tar:
                    tar.close()
                tar_index += 1
                tar_path = os.path.join(output_dir, f"dataset-{tar_index:06d}.tar")
                tar = tarfile.open(tar_path, 'w')
            data_i = dataframe.loc[i, :].to_dict()  # image,attributes,categories   Turn each line into a dictionary

            image_file_name = data_i['filename']
            all_caption = ast.literal_eval(data_i['captions'])
            sentence_level_caption = [data_i['sentence_level_captions']]
            all_caption += sentence_level_caption
            # Now need to process the captions
            if str(all_caption) == 'nan':
                continue

            caption = ''
            for single_caption in all_caption: caption += single_caption.strip('.') + "._all_retina_merged_"
            # Read the image file
            image_path = os.path.join(image_dir_path, image_file_name)
            try:
                with open(image_path, 'rb') as img_file:
                    img_data = img_file.read()
            except:
                print(f"image not found: {image_path} \n subset is {image_file_name} ")
                continue


            # Create an in-memory tarfile
            img_tarinfo = tarfile.TarInfo(name=f"{file_count:06d}.jpg")
            img_tarinfo.size = len(img_data)
            tar.addfile(img_tarinfo, io.BytesIO(img_data))

            # Add caption.txt to the tarfile
            caption_data = caption.encode('utf-8')
            caption_tarinfo = tarfile.TarInfo(name=f"{file_count:06d}.txt")
            caption_tarinfo.size = len(caption_data)
            tar.addfile(caption_tarinfo, io.BytesIO(caption_data))

            file_count += 1

    if tar:
        tar.close()

if __name__ == "__main__":
    # Argument parser setup
    parser = argparse.ArgumentParser(description="Create a WebDataset from CSV")
    parser.add_argument('--csv_files_directory', type=str, required=True, help="Path to the CSV files for all datasets")
    parser.add_argument('--output_dir', type=str, required=True, help="Directory to store the output tar files")
    parser.add_argument('--parent_datasets_path', type=str, required=True,
                        help="Path to the parent folder containing Retina Datasets folders")
    parser.add_argument('--tar_size', type=int, default=1000, help="Number of files per tar file")

    # Parse the arguments
    args = parser.parse_args()

    # Call the function with the parsed arguments
    create_webdataset(args.csv_file, args.output_dir, args.parent_dataset_path, args.tar_size)