Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
File size: 6,194 Bytes
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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# Lint as: python3
"""Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk"""
import csv
import os
import datasets
from .classes import CHARADES_CLASSES
_CITATION = """
@article{sigurdsson2016hollywood,
author = {Gunnar A. Sigurdsson and G{\"u}l Varol and Xiaolong Wang and Ivan Laptev and Ali Farhadi and Abhinav Gupta},
title = {Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding},
journal = {ArXiv e-prints},
eprint = {1604.01753},
year = {2016},
url = {http://arxiv.org/abs/1604.01753},
}
"""
_DESCRIPTION = """\
Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk. 267 different users were presented with a sentence, that includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence (like in a game of Charades). The dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos.
"""
_ANNOTATIONS_URL = "https://ai2-public-datasets.s3-us-west-2.amazonaws.com/charades/Charades.zip"
_VIDEOS_URL = {
"default": "https://ai2-public-datasets.s3-us-west-2.amazonaws.com/charades/Charades_v1.zip",
"480p": "https://ai2-public-datasets.s3-us-west-2.amazonaws.com/charades/Charades_v1_480.zip",
}
class Charades(datasets.GeneratorBasedBuilder):
"""Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk"""
BUILDER_CONFIGS = [datasets.BuilderConfig(name="default"), datasets.BuilderConfig(name="480p")]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"video_id": datasets.Value("string"),
"video": datasets.Value("string"),
"subject": datasets.Value("string"),
"scene": datasets.Value("string"),
"quality": datasets.Value("int32"),
"relevance": datasets.Value("int32"),
"verified": datasets.Value("string"),
"script": datasets.Value("string"),
"objects": datasets.features.Sequence(datasets.Value("string")),
"descriptions": datasets.features.Sequence(datasets.Value("string")),
"labels": datasets.Sequence(
datasets.features.ClassLabel(
num_classes=len(CHARADES_CLASSES), names=list(CHARADES_CLASSES.values())
)
),
"action_timings": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
"length": datasets.Value("float32"),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
annotations_path = dl_manager.download_and_extract(_ANNOTATIONS_URL)
archive = os.path.join(dl_manager.download_and_extract(_VIDEOS_URL[self.config.name]), "Charades_v1")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotation_file": os.path.join(annotations_path, "Charades", "Charades_v1_train.csv"),
"video_folder": archive,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotation_file": os.path.join(annotations_path, "Charades", "Charades_v1_test.csv"),
"video_folder": archive,
},
),
]
def _generate_examples(self, annotation_file, video_folder):
"""This function returns the examples."""
with open(annotation_file, "r", encoding="utf-8") as csv_file:
reader = csv.DictReader(csv_file)
idx = 0
for row in reader:
path = os.path.join(video_folder, row["id"] + ".mp4")
labels = []
action_timings = []
for class_label in row["actions"].split(";"):
# Skip empty action labels
if len(class_label) != 0:
# format is like: "c123 11.0 13.0"
labels.append(CHARADES_CLASSES[class_label.split(" ")[0]])
timings = list(map(float, class_label.split(" ")[1:]))
action_timings.append(timings)
yield idx, {
"video_id": row["id"],
"video": path,
"subject": row["subject"],
"scene": row["scene"],
"quality": int(row["quality"]) if len(row["quality"]) != 0 else -100,
"relevance": int(row["relevance"]) if len(row["relevance"]) != 0 else -100,
"verified": row["verified"],
"script": row["script"],
"objects": row["objects"].split(";"),
"descriptions": row["descriptions"].split(";"),
"labels": labels,
"action_timings": action_timings,
"length": row["length"],
}
idx += 1
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