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import pandas as pd
from huggingface_hub import hf_hub_url
import datasets
import os
_VERSION = datasets.Version("0.0.1")
_DESCRIPTION = "TODO"
_HOMEPAGE = "TODO"
_LICENSE = "TODO"
_CITATION = "TODO"
_FEATURES = datasets.Features(
{
"image": datasets.Image(),
"pose": datasets.Image(),
"prompt": datasets.Value("string"),
},
)
METADATA_URL = hf_hub_url(
"vision-paper/cn_pose_dataset",
filename="train.jsonl",
repo_type="dataset",
)
IMAGE_URL = hf_hub_url(
"vision-paper/cn_pose_dataset",
filename="image.zip",
repo_type="dataset",
)
POSE_URL = hf_hub_url(
"vision-paper/cn_pose_dataset",
filename="pose.zip",
repo_type="dataset",
)
_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
class cn_pose_dataset(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)
image_dir = dl_manager.download_and_extract(
IMAGE_URL
)
pose_dir = dl_manager.download_and_extract(
POSE_URL
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"metadata_path": metadata_path,
"image_dir": image_dir,
"pose_dir": pose_dir,
},
),
]
def _generate_examples(self, metadata_path, image_dir, pose_dir):
metadata = pd.read_json(metadata_path, lines=True)
for _, row in metadata.iterrows():
prompt = row["prompt"]
image_path = row["image"]
image_path = os.path.join(image_dir, image_path)
image = open(image_path, "rb").read()
pose_path = row["pose"]
pose_path = os.path.join(
pose_dir, row["pose"]
)
pose = open(pose_path, "rb").read()
yield row["image"], {
"prompt": prompt,
"image": {
"path": image_path,
"bytes": image,
},
"pose": {
"path": pose_path,
"bytes": pose,
},
}
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