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import random
import datasets
import pandas as pd
_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}
}
"""
_DESCRIPTION = """\
ANAKIN is a dataset of mANipulated videos and mAsK annotatIoNs.
"""
_HOMEPAGE = "https://github.com/AlexBlck/vader"
_LICENSE = "cc-by-4.0"
_METADATA_URL = "https://huggingface.co/datasets/AlexBlck/ANAKIN/raw/main/metadata.csv"
_FOLDERS = {
"all": ("full", "trimmed", "edited", "masks"),
"no-full": ("trimmed", "edited", "masks"),
"has-masks": ("trimmed", "edited", "masks"),
"full-masks": ("full", "trimmed", "edited", "masks"),
}
class Anakin(datasets.GeneratorBasedBuilder):
"""ANAKIN is a dataset of mANipulated videos and mAsK annotatIoNs."""
VERSION = datasets.Version("1.0.0")
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="has-masks",
version=VERSION,
description="Only samples that have masks. Without full length video.",
),
datasets.BuilderConfig(
name="full-masks",
version=VERSION,
description="Only samples that have masks. With full length video.",
),
]
DEFAULT_CONFIG_NAME = "all"
def _info(self):
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 == "has-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-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(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
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 self.config.name:
df = df[df["has-masks"] == True]
ids = df["video-id"].to_list()
random.shuffle(ids)
train_end = int(len(ids) * 0.7)
val_end = int(len(ids) * 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]
if df[df["video-id"] == idx]["has-masks"].values[0]
}
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)):
entry = df[df["video-id"] == idx]
if idx in masks.keys():
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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