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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(
{
"flawless": datasets.Image(),
"distorted": datasets.Image(),
"mask": datasets.Image(),
"reference": datasets.Image(),
"prompt": datasets.Value("string"),
},
)
METADATA_URL = hf_hub_url(
"NegarMov/Distorted_Human_Images",
filename="train.jsonl",
repo_type="dataset",
)
FLAWLESS_URL = hf_hub_url(
"NegarMov/Distorted_Human_Images",
filename="flawless.zip",
repo_type="dataset",
)
DISTORTED_URL = hf_hub_url(
"NegarMov/Distorted_Human_Images",
filename="distorted.zip",
repo_type="dataset",
)
MASK_URL = hf_hub_url(
"NegarMov/Distorted_Human_Images",
filename="mask.zip",
repo_type="dataset",
)
REFERENCE_URL = hf_hub_url(
"NegarMov/Distorted_Human_Images",
filename="reference.zip",
repo_type="dataset",
)
_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
class Distorted_Human_Images(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)
flawless_dir = dl_manager.download_and_extract(
FLAWLESS_URL
)
distorted_dir = dl_manager.download_and_extract(
DISTORTED_URL
)
mask_dir = dl_manager.download_and_extract(
MASK_URL
)
reference_dir = dl_manager.download_and_extract(
REFERENCE_URL
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"metadata_path": metadata_path,
"flawless_dir": flawless_dir,
"distorted_dir": distorted_dir,
"mask_dir": mask_dir,
"reference_dir": reference_dir,
},
),
]
def _generate_examples(self, metadata_path, flawless_dir, distorted_dir, mask_dir, reference_dir):
metadata = pd.read_json(metadata_path, lines=True)
for _, row in metadata.iterrows():
prompt = row["prompt"]
flawless_path = row["flawless"]
flawless_path = os.path.join(flawless_dir, flawless_path)
flawless = open(flawless_path, "rb").read()
distorted_path = row["distorted"]
distorted_path = os.path.join(
distorted_dir, row["distorted"]
)
distorted = open(distorted_path, "rb").read()
mask_path = row["mask"]
mask_path = os.path.join(
mask_dir, row["mask"]
)
mask = open(mask_path, "rb").read()
reference_path = row["reference"]
reference_path = os.path.join(
reference_dir, row["reference"]
)
reference = open(reference_path, "rb").read()
yield row["flawless"], {
"prompt": prompt,
"flawless": {
"path": flawless_path,
"bytes": flawless,
},
"distorted": {
"path": distorted_path,
"bytes": distorted,
},
"mask": {
"path": mask_path,
"bytes": mask,
},
"reference": {
"path": reference_path,
"bytes": reference,
},
}
|