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import datasets |
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import pandas as pd |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {selfie_and_video}, |
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author = {TrainingDataPro}, |
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year = {2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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4000 people in this dataset. Each person took a selfie on a webcam, |
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took a selfie on a mobile phone. In addition, people recorded video from |
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the phone and from the webcam, on which they pronounced a given set of numbers. |
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Includes folders corresponding to people in the dataset. Each folder includes |
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8 files (4 images and 4 videos). |
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""" |
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_NAME = 'selfie_and_video' |
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_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
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_LICENSE = "" |
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_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
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class SelfieAndVideo(datasets.GeneratorBasedBuilder): |
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"""Small sample of image-text pairs""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features({ |
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'photo_1': datasets.Image(), |
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'photo_2': datasets.Image(), |
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'video_3': datasets.Value('string'), |
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'video_4': datasets.Value('string'), |
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'photo_5': datasets.Image(), |
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'photo_6': datasets.Image(), |
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'video_7': datasets.Value('string'), |
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'video_8': datasets.Value('string'), |
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'set_id': datasets.Value('string'), |
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'worker_id': datasets.Value('string'), |
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'age': datasets.Value('int8'), |
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'country': datasets.Value('string'), |
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'gender': datasets.Value('string') |
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}), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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images = dl_manager.download(f"{_DATA}data.tar.gz") |
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annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") |
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images = dl_manager.iter_archive(images) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"images": images, |
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'annotations': annotations |
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}), |
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] |
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def _generate_examples(self, images, annotations): |
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annotations_df = pd.read_csv(annotations, sep=';') |
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images_data = pd.DataFrame(columns=['Link', 'Bytes']) |
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for idx, (image_path, image) in enumerate(images): |
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if image_path.lower().endswith('.jpg'): |
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images_data.loc[idx] = { |
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'Link': image_path, |
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'Bytes': image.read() |
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} |
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annotations_df = pd.merge(annotations_df, |
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images_data, |
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on=['Link'], |
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how='left') |
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for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])): |
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annotation = annotations_df.loc[annotations_df['WorkerId'] == |
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worker_id] |
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annotation = annotation.sort_values(['Link']) |
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data = { |
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(f'photo_{row[7][37]}' if row[7].lower().endswith('.jpg') else f'video_{row[7][37]}'): |
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({ |
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'path': row[7], |
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'bytes': row[8] |
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} if row[7].lower().endswith('.jpg') else row[7]) |
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for row in annotation.itertuples() |
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} |
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age = annotation.loc[annotation['Link'].str.lower().str.endswith( |
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'1.jpg')]['Age'].values[0] |
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country = annotation.loc[annotation['Link'].str.lower().str. |
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endswith('1.jpg')]['Country'].values[0] |
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gender = annotation.loc[annotation['Link'].str.lower().str. |
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endswith('1.jpg')]['Gender'].values[0] |
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set_id = annotation.loc[annotation['Link'].str.lower().str. |
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endswith('1.jpg')]['SetId'].values[0] |
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data['worker_id'] = worker_id |
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data['age'] = age |
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data['country'] = country |
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data['gender'] = gender |
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data['set_id'] = set_id |
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yield idx, data |
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