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import csv
import json
import os
import random
import datasets
import pandas as pd
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@misc{black2023vader,
title={VADER: Video Alignment Differencing and Retrieval},
author={Alexander Black and Simon Jenni and Tu Bui and Md. Mehrab Tanjim and Stefano Petrangeli and Ritwik Sinha and Viswanathan Swaminathan and John Collomosse},
year={2023},
eprint={2303.13193},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
ANAKIN is a dataset of mANipulated videos and mAsK annotatIoNs.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/AlexBlck/vader"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "cc-by-4.0"
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"all": "https://huggingface.co/datasets/AlexBlck/ANAKIN/raw/main/metadata.csv",
}
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class Anakin(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.0.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="all",
version=VERSION,
description="Full video, trimmed video, edited video, masks (if exists), and edit description",
),
]
DEFAULT_CONFIG_NAME = "all" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if self.config.name == "all":
features = datasets.Features(
{
"full": datasets.Value("string"),
"trimmed": datasets.Value("string"),
"edited": datasets.Value("string"),
"masks": datasets.Value("string"),
# "edit_description": datasets.Value("string"),
}
)
else: # This is an example to show how to have different features for "first_domain" and "second_domain"
features = datasets.Features(
{
"sentence": datasets.Value("string"),
"option2": datasets.Value("string"),
"second_domain_answer": datasets.Value("string")
# These are the features of your dataset like images, labels ...
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
urls = _URLS[self.config.name]
metadata_dir = dl_manager.download_and_extract(urls)
random.seed(47)
root_url = "https://huggingface.co/datasets/AlexBlck/ANAKIN/resolve/main/"
df = pd.read_csv(metadata_dir)
ids = df["video-id"].to_list()
random.shuffle(ids)
data_urls = [
{
"full": root_url + f"full/{idx}.mp4",
"trimmed": root_url + f"trimmed/{idx}.mp4",
"edited": root_url + f"edited/{idx}.mp4",
# "masks": root_url + f"masks/{idx}/",
}
for idx in ids
]
data_dir = dl_manager.download_and_extract(data_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir,
"split": "train",
"ids": ids[:342],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir,
"split": "dev",
"ids": ids[342:],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "metadata.csv"),
"split": "test",
},
),
]
def _generate_examples(self, filepath, ids, split):
for key, idx in enumerate(ids):
yield key, {
"full": filepath + f"full/{idx}.mp4",
"trimmed": filepath + f"trimmed/{idx}.mp4",
"edited": filepath + f"edited/{idx}.mp4",
# "masks": filepath + f"masks/{idx}/",
}
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