| import datasets | |
| import pandas as pd | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {portrait_and_26_photos}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| Each set includes 27 photos of people. Each person provided | |
| two types of photos: one photo in profile (portrait_1), | |
| and 26 photos from their life (photo_1, photo_2, …, photo_26). | |
| """ | |
| _NAME = 'portrait_and_26_photos' | |
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| _LICENSE = "" | |
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| class PortraitAnd26Photos(datasets.GeneratorBasedBuilder): | |
| """Small sample of image-text pairs""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| 'portrait_1': datasets.Image(), | |
| 'photo_1': datasets.Image(), | |
| 'photo_2': datasets.Image(), | |
| 'photo_3': datasets.Image(), | |
| 'photo_4': datasets.Image(), | |
| 'photo_5': datasets.Image(), | |
| 'photo_6': datasets.Image(), | |
| 'photo_7': datasets.Image(), | |
| 'photo_8': datasets.Image(), | |
| 'photo_9': datasets.Image(), | |
| 'photo_10': datasets.Image(), | |
| 'photo_11': datasets.Image(), | |
| 'photo_12': datasets.Image(), | |
| 'photo_13': datasets.Image(), | |
| 'photo_14': datasets.Image(), | |
| 'photo_15': datasets.Image(), | |
| 'photo_16': datasets.Image(), | |
| 'photo_17': datasets.Image(), | |
| 'photo_18': datasets.Image(), | |
| 'photo_19': datasets.Image(), | |
| 'photo_20': datasets.Image(), | |
| 'photo_21': datasets.Image(), | |
| 'photo_22': datasets.Image(), | |
| 'photo_23': datasets.Image(), | |
| 'photo_24': datasets.Image(), | |
| 'photo_25': datasets.Image(), | |
| 'photo_26': datasets.Image(), | |
| 'worker_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): | |
| 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=['Link', 'Bytes']) | |
| for idx, (image_path, image) in enumerate(images): | |
| images_data.loc[idx] = {'Link': image_path, 'Bytes': image.read()} | |
| annotations_df = pd.merge(annotations_df, images_data) | |
| for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])): | |
| annotation = annotations_df.loc[annotations_df['WorkerId'] == | |
| worker_id] | |
| annotation = annotation.sort_values(['Type']) | |
| data = { | |
| row[5]: { | |
| 'path': row[6], | |
| 'bytes': row[7] | |
| } for row in annotation.itertuples() | |
| } | |
| age = annotation.loc[annotation['Type'] == | |
| 'portrait_1']['Age'].values[0] | |
| country = annotation.loc[annotation['Type'] == | |
| 'portrait_1']['Country'].values[0] | |
| gender = annotation.loc[annotation['Type'] == | |
| 'portrait_1']['Gender'].values[0] | |
| data['worker_id'] = worker_id | |
| data['age'] = age | |
| data['country'] = country | |
| data['gender'] = gender | |
| yield idx, data | |