| 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=',') | |
| for idx, file_path in enumerate(files): | |
| set_id = int(file_path.split('/')[-2]) | |
| file_name = file_path.split('/')[-1] | |
| print(set_id, file_name) | |
| if 'fist' in file_name: | |
| data = { | |
| 'set_id': | |
| set_id, | |
| 'fist': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['fist'].values[0], | |
| 'four': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['four'].values[0], | |
| 'me': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['me'].values[0], | |
| 'one': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['one'].values[0], | |
| 'small': | |
| annotations_df.loc[annotations_df['set_id'] == set_id] | |
| ['small'].values[0] | |
| } | |
| yield idx, data | |