| 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 | |