Datasets:
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# Copyright 2024 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import csv
from PIL import Image
import pandas as pd
import datasets
_CITATION = """\
@inproceedings{gautam2024kvasirvqa,
title={Kvasir-VQA: A Text-Image Pair GI Tract Dataset},
author={Gautam, Sushant and Storås, Andrea and Midoglu, Cise and Hicks, Steven A. and Thambawita, Vajira and Halvorsen, Pål and Riegler, Michael A.},
booktitle={Proceedings of the First International Workshop on Vision-Language Models for Biomedical Applications (VLM4Bio '24)},
year={2024},
location={Melbourne, VIC, Australia},
publisher={ACM},
doi={10.1145/3689096.3689458}
}
"""
_DESCRIPTION = """\
The Kvasir-VQA dataset is an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations. This dataset is designed to facilitate advanced machine learning tasks in gastrointestinal (GI) diagnostics, including image captioning, Visual Question Answering (VQA), and text-based generation of synthetic medical images.
"""
_HOMEPAGE = "https://datasets.simula.no/kvasir-vqa/"
_LICENSE = "cc-by-nc-4.0"
class KvasirVQADataset(datasets.GeneratorBasedBuilder):
"""Kvasir-VQA: A Text-Image Pair GI Tract Dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="kvasir_vqa", version=VERSION, description="Kvasir-VQA dataset containing text-image pairs with question-and-answer annotations"),
]
DEFAULT_CONFIG_NAME = "kvasir_vqa"
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
"source": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"img_id": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = "."
return [
datasets.SplitGenerator(
name="raw_annotations",
gen_kwargs={
"metadata_file": os.path.join(data_dir, "metadata.csv"),
"image_dir": data_dir,
},
)
]
def _generate_examples(self, metadata_file, image_dir):
image_cache = {}
df = pd.read_csv(metadata_file, encoding='utf-8')
# shuffled_df = df.sample(frac=1, random_state=42).reset_index(drop=True)
shuffled_df = df
for idx, row in shuffled_df.iterrows():
image_file = row["file_name"]
image_path = os.path.join(image_dir, image_file)
if image_file not in image_cache:
if os.path.exists(image_path):
with open(image_path, "rb") as img_file:
image_cache[image_file] = img_file.read()
else:
continue # Skip if the image file does not exist
yield idx, {
"image": image_cache[image_file],
"source": row["source"],
"question": row["question"],
"answer": row["answer"],
"img_id": image_file.replace(".jpg", "").replace("images/", ""),
}
# RUN: datasets-cli test HuggingFaceDataset-Binary.py --save_info --all_configs
## upload to huggingface, it will save as arrow
# huggingface-cli upload SimulaMet-HOST/xxKvasir-VQA . . --repo-type dataset xxx
## then convert the arrow to parqueet
# datasets-cli convert_to_parquet SimulaMet-HOST/xxKvasir-VQA
# The file names were weird. I had to rename them to make it more readable.
# cloned the repo to local and pushed again to huggingface
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