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import pandas as pd
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from huggingface_hub import hf_hub_url
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import datasets
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import os
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_VERSION = datasets.Version("0.0.1")
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_DESCRIPTION = "TODO"
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_HOMEPAGE = "TODO"
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_LICENSE = "TODO"
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_CITATION = "TODO"
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_FEATURES = datasets.Features(
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{
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"image": datasets.Image(),
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"conditioning_image": datasets.Image(),
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"text": datasets.Value("string"),
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},
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)
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METADATA_URL = hf_hub_url(
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"YikaiZheng/BadmintonPoses205small",
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filename="caption.jsonl",
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repo_type="dataset",
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)
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IMAGES_URL = hf_hub_url(
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"YikaiZheng/BadmintonPoses205small",
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filename="images.zip",
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repo_type="dataset",
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)
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CONDITIONING_IMAGES_URL = hf_hub_url(
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"YikaiZheng/BadmintonPoses205small",
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filename="guide.zip",
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repo_type="dataset",
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)
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_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
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class Poses(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [_DEFAULT_CONFIG]
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DEFAULT_CONFIG_NAME = "default"
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=_FEATURES,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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metadata_path = dl_manager.download(METADATA_URL)
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images_dir = dl_manager.download_and_extract(IMAGES_URL)
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conditioning_images_dir = dl_manager.download_and_extract(
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CONDITIONING_IMAGES_URL
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)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"metadata_path": metadata_path,
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"images_dir": images_dir,
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"conditioning_images_dir": conditioning_images_dir,
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},
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),
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]
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def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
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metadata = pd.read_json(metadata_path, lines=True)
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for _, row in metadata.iterrows():
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text = row["text"]
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image_path = row["image"]
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image_path = os.path.join(images_dir, image_path)
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image = open(image_path, "rb").read()
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conditioning_image_path = row["conditioning_image"]
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conditioning_image_path = os.path.join(
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conditioning_images_dir, row["conditioning_image"]
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)
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conditioning_image = open(conditioning_image_path, "rb").read()
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yield row["image"], {
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"text": text,
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"image": {
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"path": image_path,
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"bytes": image,
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},
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"conditioning_image": {
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"path": conditioning_image_path,
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"bytes": conditioning_image,
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},
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} |
