import csv import json import os import random import datasets import pandas as pd 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) _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.Sequence(datasets.Image()), # "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"), # 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", } for idx in ids ] data_dir = dl_manager.download(data_urls) # data_dir = dl_manager.iter_files(data_dir) mask_dir = { idx: dl_manager.iter_archive( dl_manager.download(root_url + f"masks/{idx}.zip") ) for idx in ids } return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "files": data_dir, "masks": mask_dir, "df": df, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "files": data_dir, "masks": mask_dir, "df": df, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "files": data_dir, "masks": mask_dir, "df": df, }, ), ] def _generate_examples(self, files, masks, df): for key, sample in enumerate(files): idx = sample["trimmed"].split("/")[-1].split(".")[0] if df[df["video-id"] == idx]["has-masks"].values[0]: sample["masks"] = [m for m in masks[idx]] else: sample["masks"] = None print(sample) yield key, sample