| | 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], |
| | } |
| |
|
