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# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Rock, Paper, Scissors dataset."""
import re
from pathlib import Path
import datasets
_CITATION = """\
@ONLINE {rps,
author = "Laurence Moroney",
title = "Rock, Paper, Scissors Dataset",
month = "feb",
year = "2019",
url = "http://laurencemoroney.com/rock-paper-scissors-dataset"
}
"""
_URLS = {
'train': "https://storage.googleapis.com/download.tensorflow.org/data/rps.zip",
'test': "https://storage.googleapis.com/download.tensorflow.org/data/rps-test-set.zip"
}
_NAMES = ["rock", "paper", "scissors"]
class RockPaperScissors(datasets.GeneratorBasedBuilder):
"""Rock, Paper, Scissors dataset."""
def _info(self):
return datasets.DatasetInfo(
description="Images of hands playing rock, paper, scissor game.",
features=datasets.Features({
"file":
datasets.Value("string"),
"labels":
datasets.features.ClassLabel(names=sorted(tuple(_NAMES))),
}),
supervised_keys=("file", "labels"),
homepage="http://laurencemoroney.com/rock-paper-scissors-dataset",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"archive": data_files['train'],
}),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"archive": data_files['test'],
}),
]
def _generate_examples(self, archive):
labels = self.info.features['labels']
for i, path in enumerate(Path(archive).glob('**/*')):
if path.suffix == '.png':
yield i, dict(file=path.as_posix(), labels=labels.encode_example(path.parent.name.lower()))
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