| 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 |
|
|
