ANAKIN.py

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