File size: 2,047 Bytes
c1d9547 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import os
import csv
import datasets
from PIL import Image
import pandas as pd
class MyDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description="Dataset containing medical images with associated questions and answers.",
features=datasets.Features({
"image": datasets.Image(),
"source": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"img_id": datasets.Value("string"), }),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
df = pd.read_csv(os.path.join(self.config.data_dir, "gt.csv"),
delimiter=";")
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"df": df,
"images_dir": os.path.join(self.config.data_dir, "data")}
)
]
def _generate_examples(self, df, images_dir):
# iterate df
for idx, row in df.iterrows():
img_id = row.get("img_id")
if not img_id:
continue # Skip rows without a valid image identifier
image_path = os.path.join(images_dir, f"{img_id}")
if not os.path.exists(image_path):
continue # Skip if image file does not exist
yield idx, {
"image": image_path,
"source": row["source"],
"question": row["question"],
"answer": row["answer"],
"img_id": img_id.split(".")[0],
}
# MedVQA_test_cli.py
# from datasets import load_dataset
# dataset = load_dataset(
# "/Users/sgautam/Documents/MedVQA/MedVQA_test.py", data_dir="/Users/sgautam/Downloads/kvasir_vqa_test/", trust_remote_code=True)
# dataset.push_to_hub("SimulaMet/Kvasir-VQA-test", private=False)
# breakpoint()
# then
# python MedVQA_test_cli.py
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