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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)
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