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import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
import re
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{tobedetermined,
author = {Zihao},
title = "{Food Images}",
journal = {Nah},
year = 2022,
eid = {arXiv:Nah},
pages = {arXiv:Nah},
archivePrefix = {arXiv},
eprint = {Nah},
}
"""
_DESCRIPTION = """\
For finetunning stable diffuser with chinese food images
"""
_URL = "https://huggingface.co/datasets/zmao/chinese_food_caption/resolve/main/chinese_food_caption.tar.gz"
class FoodCaption(datasets.GeneratorBasedBuilder):
"""Chinese_food image and captions Dataset. Version 1.1."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"image": datasets.Image(),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://huggingface.co/datasets/zmao/food_img_caption",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path = dl_manager.download(_URL)
image_iters = dl_manager.iter_archive(path)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": image_iters
}
),
]
def _generate_examples(self, images):
"""This function returns the examples in the raw (text) form."""
idx = 0
#iterate through images:
for filepath, image in images:
text = filepath[14:-4]
text = text.replace('-',' ')
text = re.sub("^\d+\s|\s\d+\s|\s\d+$", " ", text)
text = text.strip()
yield idx, {
"image" : {"path": filepath, "bytes":image.read()},
"text": text
}
idx += 1
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