import urllib.parse import datasets import pandas as pd import requests _CITATION = """\ @inproceedings{Wu2020not, title={Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision}, author={Wu, Peng and Liu, jing and Shi, Yujia and Sun, Yujia and Shao, Fangtao and Wu, Zhaoyang and Yang, Zhiwei}, booktitle={European Conference on Computer Vision (ECCV)}, year={2020} } """ _DESCRIPTION = """\ Dataset for the paper "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision". \ The dataset is downloaded from the authors' website (https://roc-ng.github.io/XD-Violence/). Hosting this dataset on HuggingFace \ is just to make it easier for my own project to use this dataset. Please cite the original paper if you use this dataset. """ _NAME = "xd-violence" _HOMEPAGE = f"https://huggingface.co/datasets/jherng/{_NAME}" _LICENSE = "MIT" _URL = f"https://huggingface.co/datasets/jherng/{_NAME}/resolve/main/data/" class XDViolenceConfig(datasets.BuilderConfig): def __init__(self, **kwargs): """BuilderConfig for XD-Violence. Args: **kwargs: keyword arguments forwarded to super. """ super(XDViolenceConfig, self).__init__(**kwargs) class XDViolence(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ XDViolenceConfig( name="video", description="Video dataset", ), XDViolenceConfig( name="rgb", description="RGB visual features of the video dataset", ), ] DEFAULT_CONFIG_NAME = "video" BUILDER_CONFIG_CLASS = XDViolenceConfig CODE2LABEL = { "A": "Normal", "B1": "Fighting", "B2": "Shooting", "B4": "Riot", "B5": "Abuse", "B6": "Car accident", "G": "Explosion", } LABEL2IDX = { "Normal": 0, "Fighting": 1, "Shooting": 2, "Riot": 3, "Abuse": 4, "Car accident": 5, "Explosion": 6, } def _info(self): if self.config.name == "rgb": features = datasets.Features( { "id": datasets.Value("string"), "rgb_feats": datasets.Array3D( shape=(None, 5, 2048), dtype="float32", # (num_frames, num_crops, feature_dim) use 5 crops by default as of now ), "binary_target": datasets.ClassLabel( names=["Non-violence", "Violence"] ), "multilabel_target": datasets.Sequence( datasets.ClassLabel( names=[ "Normal", "Fighting", "Shooting", "Riot", "Abuse", "Car accident", "Explosion", ] ) ), "frame_annotations": datasets.Sequence( { "start": datasets.Value("int32"), "end": datasets.Value("int32"), } ), } ) else: # default = "video" features = datasets.Features( { "id": datasets.Value("string"), "path": datasets.Value("string"), "binary_target": datasets.ClassLabel( names=["Non-violence", "Violence"] ), "multilabel_target": datasets.Sequence( datasets.ClassLabel( names=[ "Normal", "Fighting", "Shooting", "Riot", "Abuse", "Car accident", "Explosion", ] ) ), "frame_annotations": datasets.Sequence( { "start": datasets.Value("int32"), "end": datasets.Value("int32"), } ), } ) return datasets.DatasetInfo( features=features, description=_DESCRIPTION, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): if self.config.name == "rgb": raise NotImplementedError("rgb not implemented yet") else: # Download train and test list files list_paths = { "train": dl_manager.download_and_extract( urllib.parse.urljoin(_URL, "train_list.txt") ), "test": dl_manager.download_and_extract( urllib.parse.urljoin(_URL, "test_list.txt") ), } # Download test annotation file annotation_path = dl_manager.download_and_extract( urllib.parse.urljoin(_URL, "test_annotations.txt") ) # Download videos video_urls = { "train": pd.read_csv( list_paths["train"], header=None, sep=" ", usecols=[0], names=["id"], )["id"] .apply( lambda x: urllib.parse.quote( urllib.parse.urljoin(_URL, f"video/{x.split('.mp4')[0]}.mp4"), safe=":/", ) ) .to_list(), "test": pd.read_csv( list_paths["test"], header=None, sep=" ", usecols=[0], names=["id"], )["id"] .apply( lambda x: urllib.parse.quote( urllib.parse.urljoin(_URL, f"video/{x.split('.mp4')[0]}.mp4"), safe=":/", ) ) .to_list(), } video_paths = { "train": dl_manager.download(video_urls["train"]), "test": dl_manager.download(video_urls["test"]), } # Function to read annotations annotation_readers = { "train": self._read_list, "test": self._read_list, } return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "list_path": list_paths["train"], "frame_annotation_path": None, "video_paths": video_paths["train"], "annotation_reader": annotation_readers["train"], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "list_path": list_paths["test"], "frame_annotation_path": annotation_path, "video_paths": video_paths["test"], "annotation_reader": annotation_readers["test"], }, ), ] def _generate_examples( self, list_path, frame_annotation_path, video_paths, annotation_reader ): if self.config.name == "rgb": raise NotImplementedError("rgb not implemented yet") else: ann_data = annotation_reader(list_path, frame_annotation_path) for key, (path, annotation) in enumerate(zip(video_paths, ann_data)): id = annotation["id"] binary = annotation["binary_target"] multilabel = annotation["multilabel_target"] frame_annotations = annotation.get("frame_annotations", []) yield ( key, { "id": id, "path": path, "binary_target": binary, "multilabel_target": multilabel, "frame_annotations": frame_annotations, }, ) @staticmethod def _read_list(list_path, frame_annotation_path): file_list = pd.read_csv( list_path, header=None, sep=" ", usecols=[0], names=["id"] ) file_list["id"] = file_list["id"].apply( lambda x: x.split("/")[1].split(".mp4")[0] ) file_list["binary_target"], file_list["multilabel_target"] = zip( *file_list["id"].apply(XDViolence._extract_labels) ) if not frame_annotation_path: # test set pass return file_list.to_dict("records") @classmethod def _extract_labels(cls, video_id): """Extracts labels from the video id.""" codes = video_id.split("_")[-1].split(".mp4")[0].split("-") binary = 1 if len(codes) > 1 else 0 multilabel = [ cls.LABEL2IDX[cls.CODE2LABEL[code]] for code in codes if code != "0" ] return binary, multilabel