File size: 3,440 Bytes
9d65543 cff5499 9d65543 cff5499 e7050f8 b73a645 9d65543 cff5499 e7050f8 253feb7 e7050f8 253feb7 e7050f8 253feb7 e7050f8 253feb7 e7050f8 253feb7 e7050f8 9d65543 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
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
|