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import datasets
import json
import os
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Small image-text set},
author={James Briggs},
year={2022}
}
"""
_DESCRIPTION = """\
Demo dataset for testing or showing image-text capabilities.
"""
_HOMEPAGE = "https://huggingface.co/datasets/ppp121386/Image-demo"
_LICENSE = ""
_REPO_URL = "https://huggingface.co/datasets/ppp121386/Image-demo/resolve/main/images.tar.gz"
_CAPTION_URL = "https://huggingface.co/datasets/ppp121386/Image-demo/resolve/main/caption.json"
# _CAPTION = ["a dog sitting on a bed looking at a pink wall","a brown dog sitting in front of a pink wall","two people are cross country skiing in the snow","a woman is cross country skiing in the snow","a woman is cross country skiing in the snow"]
class ImageSet(datasets.GeneratorBasedBuilder):
"""Small sample of image-text pairs"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
'text': datasets.Value("string"),
'image': datasets.Image(),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
images_archive = dl_manager.download(_REPO_URL)
image_iters = dl_manager.iter_archive(images_archive)
filepath = dl_manager.download_and_extract(_CAPTION_URL)
# caption_iters = dl_manager.iter_archive(caption_archive)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": image_iters,
"filepath": filepath
}
),
]
def _generate_examples(self, images, filepath):
""" This function returns the examples in the raw (text) form."""
_CAPTION = json.load(open(filepath, 'r'))
for idx, (imgpath, image) in enumerate(images):
# description = filepath.split('/')[-1][:-4]
# description = description.replace('_', ' ')
yield idx, {
"image": {"path": imgpath, "bytes": image.read()},
"text": _CAPTION[idx],
} |