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import pandas as pd
from huggingface_hub import hf_hub_url
import datasets
import os
_VERSION = datasets.Version("0.0.2")
_DESCRIPTION = "This dataset includes images and conditioning images for XYZ purpose."
_HOMEPAGE = "https://www.example.com"
_LICENSE = "MIT"
_CITATION = """@article{YourDataset2021,
title={Your Dataset Title},
author={Your Name},
journal={Your Journal},
year={2021}
}"""
# _FEATURES = datasets.Features({
# "image": datasets.Value("string"), # Change from datasets.Image() to Value("string") if using paths directly
# "conditioning_image": datasets.Value("string"),
# "text": datasets.Value("string"),
# })
_FEATURES = datasets.Features(
{
"image": datasets.Image(),
"conditioning_image": datasets.Image(),
"text": datasets.Value("string"),
},
)
METADATA_URL = hf_hub_url(
"spine-crook/test",
filename="train.jsonl",
repo_type="dataset",
)
IMAGES_URL = hf_hub_url(
"spine-crook/test",
filename="images.zip",
repo_type="dataset",
)
CONDITIONING_IMAGES_URL = hf_hub_url(
"spine-crook/test",
filename="conditioning_images.zip",
repo_type="dataset",
)
_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
class Test(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [_DEFAULT_CONFIG]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=_FEATURES,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
metadata_path = dl_manager.download(METADATA_URL)
images_dir = dl_manager.download_and_extract(IMAGES_URL)
conditioning_images_dir = dl_manager.download_and_extract(CONDITIONING_IMAGES_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"metadata_path": metadata_path,
"images_dir": images_dir,
"conditioning_images_dir": conditioning_images_dir,
},
),
]
def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
metadata = pd.read_json(metadata_path, lines=True)
for _, row in metadata.iterrows():
text = row["text"]
image_path = os.path.join(images_dir, row["image"])
conditioning_image_path = os.path.join(conditioning_images_dir, row["conditioning_image"])
yield row["image"], {
"text": text,
"image": image_path,
"conditioning_image": conditioning_image_path,
}
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