| # import datasets | |
| # import pandas as pd | |
| # _CITATION = """\ | |
| # @InProceedings{huggingface:dataset, | |
| # title = {hand-gesture-recognition-dataset}, | |
| # author = {TrainingDataPro}, | |
| # year = {2023} | |
| # } | |
| # """ | |
| # _DESCRIPTION = """\ | |
| # The dataset consists of videos showcasing individuals demonstrating 5 different | |
| # hand gestures (*"one", "four", "small", "fist", and "me"*). Each video captures | |
| # a person prominently displaying a single hand gesture, allowing for accurate | |
| # identification and differentiation of the gestures. | |
| # The dataset offers a diverse range of individuals performing the gestures, | |
| # enabling the exploration of variations in hand shapes, sizes, and movements | |
| # across different individuals. | |
| # The videos in the dataset are recorded in reasonable lighting conditions and | |
| # with adequate resolution, to ensure that the hand gestures can be easily | |
| # observed and studied. | |
| # """ | |
| # _NAME = 'hand-gesture-recognition-dataset' | |
| # _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| # _LICENSE = "cc-by-nc-nd-4.0" | |
| # _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| # class HandGestureRecognitionDataset(datasets.GeneratorBasedBuilder): | |
| # def _info(self): | |
| # return datasets.DatasetInfo(description=_DESCRIPTION, | |
| # features=datasets.Features({ | |
| # 'set_id': datasets.Value('int32'), | |
| # 'fist': datasets.Value('string'), | |
| # 'four': datasets.Value('string'), | |
| # 'me': datasets.Value('string'), | |
| # 'one': datasets.Value('string'), | |
| # 'small': datasets.Value('string') | |
| # }), | |
| # supervised_keys=None, | |
| # homepage=_HOMEPAGE, | |
| # citation=_CITATION, | |
| # license=_LICENSE) | |
| # def _split_generators(self, dl_manager): | |
| # files = dl_manager.download_and_extract(f"{_DATA}files.zip") | |
| # annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| # files = dl_manager.iter_files(files) | |
| # return [ | |
| # datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| # gen_kwargs={ | |
| # "files": files, | |
| # 'annotations': annotations | |
| # }), | |
| # ] | |
| # def _generate_examples(self, files, annotations): | |
| # annotations_df = pd.read_csv(annotations, sep=';') | |
| # files = sorted(files) | |
| # files = [files[i:i + 5] for i in range(0, len(files), 5)] | |
| # for idx, files_set in enumerate(files): | |
| # set_id = int(files_set[0].split('/')[2]) | |
| # data = {'set_id': set_id} | |
| # for file in files_set: | |
| # file_name = file.split('/')[3] | |
| # if 'fist' in file_name.lower(): | |
| # data['fist'] = file | |
| # elif 'four' in file_name.lower(): | |
| # data['four'] = file | |
| # elif 'me' in file_name.lower(): | |
| # data['me'] = file | |
| # elif 'one' in file_name.lower(): | |
| # data['one'] = file | |
| # elif 'small' in file_name.lower(): | |
| # data['small'] = file | |
| # yield idx, data | |