| import io |
|
|
| import datasets |
| import pandas as pd |
|
|
| _CITATION = """\ |
| @InProceedings{huggingface:dataset, |
| title = {selfies_and_id}, |
| author = {TrainingDataPro}, |
| year = {2023} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| 4083 sets, which includes 2 photos of a person from his documents and |
| 13 selfies. 571 sets of Hispanics and 3512 sets of Caucasians. |
| Photo documents contains only a photo of a person. |
| All personal information from the document is hidden. |
| """ |
| _NAME = 'selfies_and_id' |
|
|
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
|
|
| _LICENSE = "" |
|
|
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
|
|
|
|
| class SelfiesAndId(datasets.GeneratorBasedBuilder): |
| """Small sample of image-text pairs""" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| 'id_1': datasets.Image(), |
| 'id_2': datasets.Image(), |
| 'selfie_1': datasets.Image(), |
| 'selfie_2': datasets.Image(), |
| 'selfie_3': datasets.Image(), |
| 'selfie_4': datasets.Image(), |
| 'selfie_5': datasets.Image(), |
| 'selfie_6': datasets.Image(), |
| 'selfie_7': datasets.Image(), |
| 'selfie_8': datasets.Image(), |
| 'selfie_9': datasets.Image(), |
| 'selfie_10': datasets.Image(), |
| 'selfie_11': datasets.Image(), |
| 'selfie_12': datasets.Image(), |
| 'selfie_13': datasets.Image(), |
| 'user_id': datasets.Value('string'), |
| 'set_id': datasets.Value('string'), |
| 'user_race': datasets.Value('string'), |
| 'name': 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): |
| images = dl_manager.download(f"{_DATA}images.tar.gz") |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") |
| images = dl_manager.iter_archive(images) |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "images": images, |
| 'annotations': annotations |
| }), |
| ] |
|
|
| def _generate_examples(self, images, annotations): |
| annotations_df = pd.read_csv(annotations, sep=';') |
| images_data = pd.DataFrame(columns=['URL', 'Bytes']) |
| for idx, (image_path, image) in enumerate(images): |
| images_data.loc[idx] = {'URL': image_path, 'Bytes': image.read()} |
|
|
| annotations_df = pd.merge(annotations_df, |
| images_data, |
| how='left', |
| on=['URL']) |
| for idx, worker_id in enumerate(pd.unique(annotations_df['UserId'])): |
| annotation = annotations_df.loc[annotations_df['UserId'] == |
| worker_id] |
| annotation = annotation.sort_values(['FName']) |
| data = { |
| row[5].lower(): { |
| 'path': row[6], |
| 'bytes': row[10] |
| } for row in annotation.itertuples() |
| } |
|
|
| age = annotation.loc[annotation['FName'] == |
| 'ID_1']['Age'].values[0] |
| country = annotation.loc[annotation['FName'] == |
| 'ID_1']['Country'].values[0] |
| gender = annotation.loc[annotation['FName'] == |
| 'ID_1']['Gender'].values[0] |
| set_id = annotation.loc[annotation['FName'] == |
| 'ID_1']['SetId'].values[0] |
| user_race = annotation.loc[annotation['FName'] == |
| 'ID_1']['UserRace'].values[0] |
| name = annotation.loc[annotation['FName'] == |
| 'ID_1']['Name'].values[0] |
|
|
| data['user_id'] = worker_id |
| data['age'] = age |
| data['country'] = country |
| data['gender'] = gender |
| data['set_id'] = set_id |
| data['user_race'] = user_race |
| data['name'] = name |
|
|
| yield idx, data |
|
|
