| from itertools import count |
|
|
| import datasets |
| import pandas as pd |
|
|
| _CITATION = """\ |
| @InProceedings{huggingface:dataset, |
| title = {presentation-attack-detection-2d-dataset}, |
| author = {TrainingDataPro}, |
| year = {2023} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| The dataset consists of photos of individuals and videos of him/her wearing printed 2D |
| mask with cut-out holes for eyes. Videos are filmed in different lightning conditions |
| and in different places (*indoors, outdoors*), a person moves his/her head left, right, |
| up and down. Each video in the dataset has an approximate duration of 15-17 seconds. |
| """ |
| _NAME = "presentation-attack-detection-2d-dataset" |
|
|
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
|
|
| _LICENSE = "" |
|
|
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
|
|
|
|
| class PresentationAttackDetection2dDataset(datasets.GeneratorBasedBuilder): |
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "photo": datasets.Image(), |
| "video": datasets.Value("string"), |
| "worker_id": datasets.Value("string"), |
| "set_id": datasets.Value("string"), |
| "age": datasets.Value("int8"), |
| "country": datasets.Value("string"), |
| "gender": datasets.Value("string"), |
| } |
| ), |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| attacks = dl_manager.download(f"{_DATA}attacks.tar.gz") |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") |
| attacks = dl_manager.iter_archive(attacks) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={"attacks": attacks, "annotations": annotations}, |
| ), |
| ] |
|
|
| def _generate_examples(self, attacks, annotations): |
| annotations_df = pd.read_csv(annotations, sep=",") |
| for idx, (image_path, image) in enumerate(attacks): |
| if image_path.endswith("jpg"): |
| yield idx, { |
| "photo": {"path": image_path, "bytes": image.read()}, |
| "video": annotations_df.loc[ |
| annotations_df["image"] == image_path |
| ]["video"].values[0], |
| "worker_id": annotations_df.loc[ |
| annotations_df["image"] == image_path |
| ]["worker_id"].values[0], |
| "set_id": annotations_df.loc[ |
| annotations_df["image"] == image_path |
| ]["set_id"].values[0], |
| "age": annotations_df.loc[ |
| annotations_df["image"] == image_path |
| ]["age"].values[0], |
| "country": annotations_df.loc[ |
| annotations_df["image"] == image_path |
| ]["country"].values[0], |
| "gender": annotations_df.loc[ |
| annotations_df["image"] == image_path |
| ]["gender"].values[0], |
| } |
|
|
