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import csv
import json
import os
import random
import datasets
import pandas as pd
from PIL import Image
from torchvision.io import read_video
# 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)
_METADATA_URL = "https://huggingface.co/datasets/AlexBlck/ANAKIN/raw/main/metadata.csv"
_FOLDERS = {
"all": ("full", "trimmed", "edited", "masks"),
"no-full": ("trimmed", "edited", "masks"),
"trimmed-edited-masks": ("trimmed", "edited", "masks"),
"full-trimmed-edited-masks": ("full", "trimmed", "edited", "masks"),
}
# 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",
),
datasets.BuilderConfig(
name="no-full",
version=VERSION,
description="Trimmed video, edited video, masks (if exists), and edit description",
),
datasets.BuilderConfig(
name="trimmed-edited-masks",
version=VERSION,
description="Only samples that have masks. Without full length video.",
),
datasets.BuilderConfig(
name="full-trimmed-edited-masks",
version=VERSION,
description="Only samples that have masks. With full length video.",
),
]
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.Sequence(datasets.Image()),
"task": datasets.Value("string"),
"start-time": datasets.Value("int32"),
"end-time": datasets.Value("int32"),
"manipulation-type": datasets.Value("string"),
"editor-id": datasets.Value("string"),
}
)
elif self.config.name == "no-full":
features = datasets.Features(
{
"trimmed": datasets.Value("string"),
"edited": datasets.Value("string"),
"masks": datasets.Sequence(datasets.Image()),
"task": datasets.Value("string"),
"manipulation-type": datasets.Value("string"),
"editor-id": datasets.Value("string"),
}
)
elif self.config.name == "trimmed-edited-masks":
features = datasets.Features(
{
"trimmed": datasets.Value("string"),
"edited": datasets.Value("string"),
"masks": datasets.Sequence(datasets.Image()),
"task": datasets.Value("string"),
"manipulation-type": datasets.Value("string"),
"editor-id": datasets.Value("string"),
}
)
elif self.config.name == "full-trimmed-edited-masks":
features = datasets.Features(
{
"full": datasets.Value("string"),
"trimmed": datasets.Value("string"),
"edited": datasets.Value("string"),
"masks": datasets.Sequence(datasets.Image()),
"task": datasets.Value("string"),
"start-time": datasets.Value("int32"),
"end-time": datasets.Value("int32"),
"manipulation-type": datasets.Value("string"),
"editor-id": datasets.Value("string"),
}
)
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
metadata_dir = dl_manager.download(_METADATA_URL)
folders = _FOLDERS[self.config.name]
random.seed(47)
root_url = "https://huggingface.co/datasets/AlexBlck/ANAKIN/resolve/main/"
df = pd.read_csv(metadata_dir)
if "full" in folders:
df = df[df["full-available"] == True]
if "masks" in folders:
df = df[df["has-masks"] == True]
ids = df["video-id"].to_list()
random.shuffle(ids)
train_end = int(len(df) * 0.7)
val_end = int(len(df) * 0.8)
split_ids = {
datasets.Split.TRAIN: ids[:train_end],
datasets.Split.VALIDATION: ids[train_end:val_end],
datasets.Split.TEST: ids[val_end:],
}
data_dir = {}
mask_dir = {}
for split in [
datasets.Split.TRAIN,
datasets.Split.VALIDATION,
datasets.Split.TEST,
]:
data_urls = [
{
f"{folder}": root_url + f"{folder}/{idx}.mp4"
for folder in folders
if folder != "masks"
}
for idx in split_ids[split]
]
data_dir[split] = dl_manager.download(data_urls)
mask_dir[split] = {
idx: dl_manager.iter_archive(
dl_manager.download(root_url + f"masks/{idx}.zip")
)
for idx in split_ids[split]
}
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"files": data_dir[split],
"masks": mask_dir[split],
"df": df,
"ids": split_ids[split],
"return_time": "full" in folders,
},
)
for split in [
datasets.Split.TRAIN,
datasets.Split.VALIDATION,
datasets.Split.TEST,
]
]
def _generate_examples(self, files, masks, df, ids, return_time):
for key, (idx, sample) in enumerate(zip(ids, files)):
print(idx)
entry = df[df["video-id"] == idx]
print(entry)
if entry["has-masks"].values[0]:
sample["masks"] = [
{"path": p, "bytes": im.read()} for p, im in masks[idx]
]
else:
sample["masks"] = None
sample["task"] = entry["task"].values[0]
sample["manipulation-type"] = entry["manipulation-type"].values[0]
sample["editor-id"] = entry["editor-id"].values[0]
if return_time:
sample["start-time"] = entry["start-time"].values[0]
sample["end-time"] = entry["end-time"].values[0]
yield key, sample
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