Datasets:
Languages:
English
Tags:
video understanding
File size: 4,313 Bytes
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import os
import datasets
import numpy as np
_DESCRIPTION = """\
GTEA is composed of 50 recorded videos of 25 participants making two different mixed salads. The videos are captured by a camera with a top-down view onto the work-surface. The participants are provided with recipe steps which are randomly sampled from a statistical recipe model.
"""
_CITATION = """\
@inproceedings{stein2013combining,
title={Combining embedded accelerometers with computer vision for recognizing food preparation activities},
author={Stein, Sebastian and McKenna, Stephen J},
booktitle={Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing},
pages={729--738},
year={2013}
}
"""
_HOMEPAGE = ""
_LICENSE = "xxx"
_URLS = {"full": "https://huggingface.co/datasets/dinggd/gtea/resolve/main/gtea.zip"}
class GTEA(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="split1", version=VERSION, description="Cross Validation Split1"
),
datasets.BuilderConfig(
name="split2", version=VERSION, description="Cross Validation Split2"
),
datasets.BuilderConfig(
name="split3", version=VERSION, description="Cross Validation Split3"
),
datasets.BuilderConfig(
name="split4", version=VERSION, description="Cross Validation Split4"
),
]
DEFAULT_CONFIG_NAME = "1"
def _info(self):
features = datasets.Features(
{
"video_id": datasets.Value("string"),
"video_feature": datasets.Array2D(shape=(None, 2048), dtype="float32"),
"video_label": datasets.Sequence(datasets.Value(dtype="int32")),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls_to_download = _URLS
data_dir = dl_manager.download_and_extract(urls_to_download)["full"]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(
data_dir, f"gtea/splits/train.{self.config.name}.bundle"
),
"featurefolder": os.path.join(data_dir, "gtea/features"),
"gtfolder": os.path.join(data_dir, "gtea/groundTruth"),
"mappingpath": os.path.join(data_dir, "gtea/mapping.txt"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(
data_dir, f"gtea/splits/test.{self.config.name}.bundle"
),
"featurefolder": os.path.join(data_dir, "gtea/features"),
"gtfolder": os.path.join(data_dir, "gtea/groundTruth"),
"mappingpath": os.path.join(data_dir, "gtea/mapping.txt"),
},
),
]
def _generate_examples(self, filepath, featurefolder, gtfolder, mappingpath):
with open(mappingpath, "r") as f:
actions = f.read().splitlines()
actions_dict = {}
for a in actions:
actions_dict[a.split()[1]] = int(a.split()[0])
with open(filepath, "r") as f:
lines = f.read().splitlines()
for key, line in enumerate(lines):
vid = line[:-4]
featurepath = os.path.join(featurefolder, f"{vid}.npy")
gtpath = os.path.join(gtfolder, line)
feature = np.load(featurepath).T # T x D
with open(gtpath, "r") as f:
content = f.read().splitlines()
label = np.zeros(min(np.shape(feature)[1], len(content)))
for i in range(len(label)):
label[i] = actions_dict[content[i]]
yield key, {
"video_id": vid,
"video_feature": feature,
"video_label": label,
}
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