| 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 | |