"""OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection This dataset builder provides unified access to the OmniFall benchmark, which integrates: - OF-Staged (OF-Sta): 8 public staged fall detection datasets (~14h single-view) - OF-In-the-Wild (OF-ItW): Curated genuine accident videos from OOPS (~2.7h) - OF-Synthetic (OF-Syn): 12,000 synthetic videos generated with Wan 2.2 (~17h) All components share a 16-class activity taxonomy. Staged datasets use classes 0-9, while OF-ItW and OF-Syn use the full 0-15 range. """ import warnings import pandas as pd import datasets from datasets import ( BuilderConfig, GeneratorBasedBuilder, Features, Value, ClassLabel, Sequence, SplitGenerator, Split, Video, ) _CITATION = """\ @misc{omnifall, title={OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection}, author={David Schneider and Zdravko Marinov and Rafael Baur and Zeyun Zhong and Rodi D\\\"uger and Rainer Stiefelhagen}, year={2025}, eprint={2505.19889}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2505.19889}, } """ _DESCRIPTION = """\ OmniFall is a comprehensive benchmark that unifies staged, in-the-wild, and synthetic fall detection datasets under a common 16-class activity taxonomy. """ _HOMEPAGE = "https://huggingface.co/datasets/simplexsigil2/omnifall" _LICENSE = "cc-by-nc-4.0" # 16 activity classes shared across all components _ACTIVITY_LABELS = [ "walk", # 0 "fall", # 1 "fallen", # 2 "sit_down", # 3 "sitting", # 4 "lie_down", # 5 "lying", # 6 "stand_up", # 7 "standing", # 8 "other", # 9 "kneel_down", # 10 "kneeling", # 11 "squat_down", # 12 "squatting", # 13 "crawl", # 14 "jump", # 15 ] # Demographic and scene metadata categories (OF-Syn only) _AGE_GROUPS = [ "toddlers_1_4", "children_5_12", "teenagers_13_17", "young_adults_18_34", "middle_aged_35_64", "elderly_65_plus", ] _GENDERS = ["male", "female"] _SKIN_TONES = [f"mst{i}" for i in range(1, 11)] _ETHNICITIES = ["white", "black", "asian", "hispanic_latino", "aian", "nhpi", "mena"] _BMI_BANDS = ["underweight", "normal", "overweight", "obese"] _HEIGHT_BANDS = ["short", "avg", "tall"] _ENVIRONMENTS = ["indoor", "outdoor"] _CAMERA_ELEVATIONS = ["eye", "low", "high", "top"] _CAMERA_AZIMUTHS = ["front", "rear", "left", "right"] _CAMERA_DISTANCES = ["medium", "far"] _CAMERA_SHOTS = ["static_wide", "static_medium_wide"] _SPEEDS = ["24fps_rt", "25fps_rt", "30fps_rt", "std_rt"] # The 8 staged datasets _STAGED_DATASETS = [ "caucafall", "cmdfall", "edf", "gmdcsa24", "le2i", "mcfd", "occu", "up_fall", ] # Label CSV file paths (relative to repo root) _STAGED_LABEL_FILES = [f"labels/{name}.csv" for name in [ "caucafall", "cmdfall", "edf", "GMDCSA24", "le2i", "mcfd", "occu", "up_fall", ]] _ITW_LABEL_FILE = "labels/OOPS.csv" _SYN_LABEL_FILE = "labels/of-syn.csv" _SYN_VIDEO_ARCHIVE = "data_files/omnifall-synthetic_av1.tar" # ---- Feature schema definitions ---- def _core_features(): """7-column schema for staged/OOPS data.""" return Features({ "path": Value("string"), "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS), "start": Value("float32"), "end": Value("float32"), "subject": Value("int32"), "cam": Value("int32"), "dataset": Value("string"), }) def _syn_features(): """19-column schema for synthetic data (core + demographic/scene metadata).""" return Features({ "path": Value("string"), "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS), "start": Value("float32"), "end": Value("float32"), "subject": Value("int32"), "cam": Value("int32"), "dataset": Value("string"), # Demographic metadata "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS), "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS), "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES), "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES), "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS), "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS), # Scene metadata "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS), "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS), "speed": ClassLabel(num_classes=4, names=_SPEEDS), "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS), "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS), "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES), }) def _syn_metadata_features(): """Feature schema for OF-Syn metadata config (video-level, no temporal segments).""" return Features({ "path": Value("string"), "dataset": Value("string"), "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS), "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS), "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES), "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES), "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS), "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS), "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS), "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS), "speed": ClassLabel(num_classes=4, names=_SPEEDS), "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS), "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS), "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES), }) def _syn_framewise_features(): """Feature schema for OF-Syn frame-wise labels (81 labels per video).""" return Features({ "path": Value("string"), "dataset": Value("string"), "frame_labels": Sequence( ClassLabel(num_classes=16, names=_ACTIVITY_LABELS), length=81 ), "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS), "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS), "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES), "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES), "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS), "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS), "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS), "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS), "speed": ClassLabel(num_classes=4, names=_SPEEDS), "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS), "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS), "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES), }) def _paths_only_features(): """Minimal feature schema for paths-only mode.""" return Features({"path": Value("string")}) # ---- Config ---- class OmniFallConfig(BuilderConfig): """BuilderConfig for OmniFall dataset. Args: config_type: What kind of data to load. "labels" - All labels in a single split (no train/val/test). "split" - Train/val/test splits from split CSV files. "metadata" - Video-level metadata (OF-Syn only). "framewise" - Frame-wise HDF5 labels (OF-Syn only). data_source: Which component(s) to load. "staged" - 8 staged lab datasets "itw" - OOPS in-the-wild "syn" - OF-Syn synthetic "staged+itw" - Staged and OOPS combined Individual dataset names (e.g. "cmdfall") for single datasets. split_type: Split strategy. "cs" / "cv" for staged/OOPS, "random" / "cross_age" / etc. for synthetic. train_source: For cross-domain configs, overrides data_source for train/val. test_source: For cross-domain configs, overrides data_source for test. test_split_type: For cross-domain configs, overrides split_type for test. paths_only: If True, only return video paths (no label merging). framewise: If True, load frame-wise labels from HDF5 (OF-Syn only). include_video: If True, download and include video files (OF-Syn only). decode_video: If True (default), use Video() feature for auto-decoding. If False, return absolute file path as string. deprecated_alias_for: If set, this config is a deprecated alias. """ def __init__( self, config_type="labels", data_source="staged+itw", split_type=None, train_source=None, test_source=None, test_split_type=None, paths_only=False, framewise=False, include_video=False, decode_video=True, deprecated_alias_for=None, **kwargs, ): super().__init__(**kwargs) self.config_type = config_type self.data_source = data_source self.split_type = split_type self.train_source = train_source self.test_source = test_source self.test_split_type = test_split_type self.paths_only = paths_only self.framewise = framewise self.include_video = include_video self.decode_video = decode_video self.deprecated_alias_for = deprecated_alias_for @property def is_crossdomain(self): return self.train_source is not None def _make_config(name, description, **kwargs): """Helper to create a config with consistent version.""" return OmniFallConfig( name=name, version=datasets.Version("2.0.0"), description=description, **kwargs, ) # ---- Config definitions ---- _LABELS_CONFIGS = [ _make_config( "labels", "All staged + OOPS labels (52k segments, 7 columns). Default config.", config_type="labels", data_source="staged+itw", ), _make_config( "labels-syn", "OF-Syn labels with demographic metadata (19k segments, 19 columns).", config_type="labels", data_source="syn", ), _make_config( "metadata-syn", "OF-Syn video-level metadata (12k videos, no temporal segments).", config_type="metadata", data_source="syn", ), _make_config( "framewise-syn", "OF-Syn frame-wise labels from HDF5 (81 labels per video).", config_type="framewise", data_source="syn", framewise=True, ), ] _AGGREGATE_CONFIGS = [ _make_config( "cs", "Cross-subject splits for all staged + OOPS datasets combined.", config_type="split", data_source="staged+itw", split_type="cs", ), _make_config( "cv", "Cross-view splits for all staged + OOPS datasets combined.", config_type="split", data_source="staged+itw", split_type="cv", ), ] _PRIMARY_CONFIGS = [ _make_config( "of-sta-cs", "OF-Staged: 8 staged datasets, cross-subject splits.", config_type="split", data_source="staged", split_type="cs", ), _make_config( "of-sta-cv", "OF-Staged: 8 staged datasets, cross-view splits.", config_type="split", data_source="staged", split_type="cv", ), _make_config( "of-itw", "OF-ItW: OOPS-Fall in-the-wild genuine accidents.", config_type="split", data_source="itw", split_type="cs", ), _make_config( "of-syn", "OF-Syn: synthetic, random 80/10/10 split.", config_type="split", data_source="syn", split_type="random", ), _make_config( "of-syn-cross-age", "OF-Syn: cross-age split (train: adults, test: children/elderly).", config_type="split", data_source="syn", split_type="cross_age", ), _make_config( "of-syn-cross-ethnicity", "OF-Syn: cross-ethnicity split.", config_type="split", data_source="syn", split_type="cross_ethnicity", ), _make_config( "of-syn-cross-bmi", "OF-Syn: cross-BMI split (train: normal/underweight, test: obese).", config_type="split", data_source="syn", split_type="cross_bmi", ), ] _CROSSDOMAIN_CONFIGS = [ _make_config( "of-sta-itw-cs", "Cross-domain: train/val on staged CS, test on OOPS.", config_type="split", data_source="staged", split_type="cs", train_source="staged", test_source="itw", test_split_type="cs", ), _make_config( "of-sta-itw-cv", "Cross-domain: train/val on staged CV, test on OOPS.", config_type="split", data_source="staged", split_type="cv", train_source="staged", test_source="itw", test_split_type="cv", ), _make_config( "of-syn-itw", "Cross-domain: train/val on OF-Syn random, test on OOPS.", config_type="split", data_source="syn", split_type="random", train_source="syn", test_source="itw", test_split_type="cs", ), ] _INDIVIDUAL_CONFIGS = [ _make_config( name, f"{name} dataset with cross-subject splits.", config_type="split", data_source=name, split_type="cs", ) for name in _STAGED_DATASETS ] # Deprecated aliases: defined with full correct attributes so _info() works # immediately (HF calls _info() during __init__, before any custom init code). _DEPRECATED_ALIASES = { "cs-staged": "of-sta-cs", "cv-staged": "of-sta-cv", "cs-staged-wild": "of-sta-itw-cs", "cv-staged-wild": "of-sta-itw-cv", "OOPS": "of-itw", } # Build a lookup from config name to config object _ALL_NAMED_CONFIGS = { cfg.name: cfg for cfg in ( _LABELS_CONFIGS + _AGGREGATE_CONFIGS + _PRIMARY_CONFIGS + _CROSSDOMAIN_CONFIGS + _INDIVIDUAL_CONFIGS ) } _DEPRECATED_CONFIGS = [] for _old_name, _new_name in _DEPRECATED_ALIASES.items(): _target = _ALL_NAMED_CONFIGS[_new_name] _DEPRECATED_CONFIGS.append( _make_config( _old_name, f"DEPRECATED: Use '{_new_name}' instead.", config_type=_target.config_type, data_source=_target.data_source, split_type=_target.split_type, train_source=_target.train_source, test_source=_target.test_source, test_split_type=_target.test_split_type, paths_only=_target.paths_only, framewise=_target.framewise, include_video=_target.include_video, decode_video=_target.decode_video, deprecated_alias_for=_new_name, ) ) # ---- Builder ---- class OmniFall(GeneratorBasedBuilder): """OmniFall unified fall detection benchmark builder.""" VERSION = datasets.Version("2.0.0") BUILDER_CONFIG_CLASS = OmniFallConfig BUILDER_CONFIGS = ( _LABELS_CONFIGS + _AGGREGATE_CONFIGS + _PRIMARY_CONFIGS + _CROSSDOMAIN_CONFIGS + _INDIVIDUAL_CONFIGS + _DEPRECATED_CONFIGS ) DEFAULT_CONFIG_NAME = "labels" def _info(self): """Return dataset metadata and feature schema.""" cfg = self.config if cfg.config_type == "metadata": features = _syn_metadata_features() elif cfg.framewise: features = _syn_framewise_features() elif cfg.paths_only: features = _paths_only_features() elif cfg.is_crossdomain: # Cross-domain configs mix sources, use common 7-col schema features = _core_features() elif cfg.data_source == "syn": features = _syn_features() else: features = _core_features() if cfg.include_video: features["video"] = Video() if cfg.decode_video else Value("string") return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) # ---- Split generators ---- def _split_generators(self, dl_manager): cfg = self.config # Emit deprecation warning if cfg.deprecated_alias_for: warnings.warn( f"Config '{cfg.name}' is deprecated. " f"Use '{cfg.deprecated_alias_for}' instead.", DeprecationWarning, stacklevel=2, ) # Labels configs: all data in a single "train" split if cfg.config_type == "labels": return self._labels_splits(cfg, dl_manager) # Metadata config if cfg.config_type == "metadata": metadata_path = dl_manager.download("videos/metadata.csv") return [ SplitGenerator( name=Split.TRAIN, gen_kwargs={"mode": "metadata", "metadata_path": metadata_path}, ), ] # Framewise config (no split, all data) if cfg.config_type == "framewise": archive_path = dl_manager.download_and_extract( "data_files/syn_frame_wise_labels.tar.zst" ) metadata_path = dl_manager.download("videos/metadata.csv") return [ SplitGenerator( name=Split.TRAIN, gen_kwargs={ "mode": "framewise", "hdf5_dir": archive_path, "metadata_path": metadata_path, "split_file": None, }, ), ] # Split configs (train/val/test) if cfg.config_type == "split": return self._split_config_generators(cfg, dl_manager) raise ValueError(f"Unknown config_type: {cfg.config_type}") def _labels_splits(self, cfg, dl_manager): """Generate split generators for labels-type configs.""" if cfg.data_source == "syn": filepath = dl_manager.download(_SYN_LABEL_FILE) return [ SplitGenerator( name=Split.TRAIN, gen_kwargs={"mode": "csv_direct", "filepath": filepath}, ), ] elif cfg.data_source == "staged+itw": filepaths = dl_manager.download(_STAGED_LABEL_FILES + [_ITW_LABEL_FILE]) return [ SplitGenerator( name=Split.TRAIN, gen_kwargs={"mode": "csv_multi", "filepaths": filepaths}, ), ] else: raise ValueError(f"Unsupported data_source for labels: {cfg.data_source}") def _split_config_generators(self, cfg, dl_manager): """Generate split generators for train/val/test split configs.""" if cfg.is_crossdomain: return self._crossdomain_splits(cfg, dl_manager) if cfg.data_source == "syn": return self._syn_splits(cfg, dl_manager) elif cfg.data_source == "staged": return self._staged_splits(cfg, dl_manager) elif cfg.data_source == "itw": return self._itw_splits(cfg, dl_manager) elif cfg.data_source == "staged+itw": return self._aggregate_splits(cfg, dl_manager) elif cfg.data_source in _STAGED_DATASETS: return self._individual_splits(cfg, dl_manager) else: raise ValueError(f"Unknown data_source: {cfg.data_source}") def _staged_split_files(self, split_type, split_name): """Return list of split CSV paths for all 8 staged datasets.""" return [f"splits/{split_type}/{ds}/{split_name}.csv" for ds in _STAGED_DATASETS] def _make_split_merge_generators(self, split_files_per_split, label_files, dl_manager, video_dir=None): """Helper to create train/val/test SplitGenerators for split_merge mode. Args: split_files_per_split: dict mapping split name to list of relative paths. label_files: list of relative label file paths. dl_manager: download manager for resolving paths. video_dir: path to extracted video directory, or None. """ resolved_labels = dl_manager.download(label_files) return [ SplitGenerator( name=split_enum, gen_kwargs={ "mode": "split_merge", "split_files": dl_manager.download(split_files_per_split[csv_name]), "label_files": resolved_labels, "video_dir": video_dir, }, ) for split_enum, csv_name in [ (Split.TRAIN, "train"), (Split.VALIDATION, "val"), (Split.TEST, "test"), ] ] def _staged_splits(self, cfg, dl_manager): """OF-Staged: 8 datasets combined with CS or CV splits.""" st = cfg.split_type return self._make_split_merge_generators( {sn: self._staged_split_files(st, sn) for sn in ("train", "val", "test")}, _STAGED_LABEL_FILES, dl_manager, ) def _itw_splits(self, cfg, dl_manager): """OF-ItW: OOPS-Fall (CS=CV identical).""" st = cfg.split_type return self._make_split_merge_generators( {sn: [f"splits/{st}/OOPS/{sn}.csv"] for sn in ("train", "val", "test")}, [_ITW_LABEL_FILE], dl_manager, ) def _aggregate_splits(self, cfg, dl_manager): """All staged + OOPS combined (cs or cv).""" st = cfg.split_type all_labels = _STAGED_LABEL_FILES + [_ITW_LABEL_FILE] return self._make_split_merge_generators( {sn: self._staged_split_files(st, sn) + [f"splits/{st}/OOPS/{sn}.csv"] for sn in ("train", "val", "test")}, all_labels, dl_manager, ) def _individual_splits(self, cfg, dl_manager): """Individual dataset with CS splits.""" ds_name = cfg.data_source label_file_map = { "caucafall": "labels/caucafall.csv", "cmdfall": "labels/cmdfall.csv", "edf": "labels/edf.csv", "gmdcsa24": "labels/GMDCSA24.csv", "le2i": "labels/le2i.csv", "mcfd": "labels/mcfd.csv", "occu": "labels/occu.csv", "up_fall": "labels/up_fall.csv", } label_file = label_file_map[ds_name] st = cfg.split_type return self._make_split_merge_generators( {sn: [f"splits/{st}/{ds_name}/{sn}.csv"] for sn in ("train", "val", "test")}, [label_file], dl_manager, ) def _syn_splits(self, cfg, dl_manager): """OF-Syn split strategies.""" st = cfg.split_type split_dir = f"splits/syn/{st}" # Download video archive if requested video_dir = None if cfg.include_video: video_dir = dl_manager.download_and_extract(_SYN_VIDEO_ARCHIVE) if cfg.framewise: archive_path = dl_manager.download_and_extract( "data_files/syn_frame_wise_labels.tar.zst" ) metadata_path = dl_manager.download("videos/metadata.csv") split_files = dl_manager.download( {sn: f"{split_dir}/{sn}.csv" for sn in ("train", "val", "test")} ) return [ SplitGenerator( name=split_enum, gen_kwargs={ "mode": "framewise", "hdf5_dir": archive_path, "metadata_path": metadata_path, "split_file": split_files[csv_name], }, ) for split_enum, csv_name in [ (Split.TRAIN, "train"), (Split.VALIDATION, "val"), (Split.TEST, "test"), ] ] if cfg.paths_only: split_files = dl_manager.download( {sn: f"{split_dir}/{sn}.csv" for sn in ("train", "val", "test")} ) return [ SplitGenerator( name=split_enum, gen_kwargs={ "mode": "paths_only", "split_file": split_files[csv_name], }, ) for split_enum, csv_name in [ (Split.TRAIN, "train"), (Split.VALIDATION, "val"), (Split.TEST, "test"), ] ] return self._make_split_merge_generators( {sn: [f"{split_dir}/{sn}.csv"] for sn in ("train", "val", "test")}, [_SYN_LABEL_FILE], dl_manager, video_dir=video_dir, ) def _crossdomain_splits(self, cfg, dl_manager): """Cross-domain configs: train/val from one source, test from another.""" train_st = cfg.split_type test_st = cfg.test_split_type or "cs" # Download video archive if requested and train source is syn video_dir = None if cfg.include_video and cfg.train_source == "syn": video_dir = dl_manager.download_and_extract(_SYN_VIDEO_ARCHIVE) # Determine train/val files and labels if cfg.train_source == "staged": train_split_files = { sn: self._staged_split_files(train_st, sn) for sn in ("train", "val") } train_labels = _STAGED_LABEL_FILES elif cfg.train_source == "syn": train_split_files = { sn: [f"splits/syn/{train_st}/{sn}.csv"] for sn in ("train", "val") } train_labels = [_SYN_LABEL_FILE] else: raise ValueError(f"Unsupported train_source: {cfg.train_source}") # Determine test files and labels if cfg.test_source == "itw": test_split_files = [f"splits/{test_st}/OOPS/test.csv"] test_labels = [_ITW_LABEL_FILE] else: raise ValueError(f"Unsupported test_source: {cfg.test_source}") # Download all paths resolved_train_labels = dl_manager.download(train_labels) resolved_test_labels = dl_manager.download(test_labels) resolved_test_splits = dl_manager.download(test_split_files) return [ SplitGenerator( name=Split.TRAIN, gen_kwargs={ "mode": "split_merge", "split_files": dl_manager.download(train_split_files["train"]), "label_files": resolved_train_labels, "video_dir": video_dir, }, ), SplitGenerator( name=Split.VALIDATION, gen_kwargs={ "mode": "split_merge", "split_files": dl_manager.download(train_split_files["val"]), "label_files": resolved_train_labels, "video_dir": video_dir, }, ), SplitGenerator( name=Split.TEST, gen_kwargs={ "mode": "split_merge", "split_files": resolved_test_splits, "label_files": resolved_test_labels, "video_dir": None, # test source (itw) has no hosted videos }, ), ] # ---- Example generators ---- def _generate_examples(self, mode, **kwargs): """Dispatch to the appropriate generator based on mode.""" if mode == "csv_direct": yield from self._gen_csv_direct(**kwargs) elif mode == "csv_multi": yield from self._gen_csv_multi(**kwargs) elif mode == "split_merge": yield from self._gen_split_merge(**kwargs) elif mode == "metadata": yield from self._gen_metadata(**kwargs) elif mode == "framewise": yield from self._gen_framewise(**kwargs) elif mode == "paths_only": yield from self._gen_paths_only(**kwargs) else: raise ValueError(f"Unknown generation mode: {mode}") def _gen_csv_direct(self, filepath): """Load a single CSV file directly.""" df = pd.read_csv(filepath) for idx, row in df.iterrows(): yield idx, self._row_to_example(row) def _gen_csv_multi(self, filepaths): """Load and concatenate multiple CSV files.""" dfs = [pd.read_csv(fp) for fp in filepaths] df = pd.concat(dfs, ignore_index=True) for idx, row in df.iterrows(): yield idx, self._row_to_example(row) def _gen_split_merge(self, split_files, label_files, video_dir=None): """Load split paths, merge with labels, yield examples.""" import os split_dfs = [pd.read_csv(sf) for sf in split_files] split_df = pd.concat(split_dfs, ignore_index=True) if self.config.paths_only: for idx, row in split_df.iterrows(): yield idx, {"path": row["path"]} return label_dfs = [pd.read_csv(lf) for lf in label_files] labels_df = pd.concat(label_dfs, ignore_index=True) merged_df = pd.merge(split_df, labels_df, on="path", how="left") for idx, row in merged_df.iterrows(): example = self._row_to_example(row) if video_dir is not None: example["video"] = os.path.join(video_dir, row["path"] + ".mp4") yield idx, example def _gen_metadata(self, metadata_path): """Load OF-Syn video-level metadata.""" df = pd.read_csv(metadata_path) metadata_cols = [ "path", "age_group", "gender_presentation", "monk_skin_tone", "race_ethnicity_omb", "bmi_band", "height_band", "environment_category", "camera_shot", "speed", "camera_elevation", "camera_azimuth", "camera_distance", ] available_cols = [c for c in metadata_cols if c in df.columns] df = df[available_cols].drop_duplicates(subset=["path"]).reset_index(drop=True) df["dataset"] = "of-syn" for idx, row in df.iterrows(): yield idx, self._row_to_example(row) def _gen_framewise(self, hdf5_dir, metadata_path, split_file=None): """Load frame-wise labels from HDF5 files with metadata.""" import h5py import tarfile from pathlib import Path metadata_df = pd.read_csv(metadata_path) valid_paths = None if split_file is not None: split_df = pd.read_csv(split_file) valid_paths = set(split_df["path"].tolist()) hdf5_path = Path(hdf5_dir) metadata_fields = [ "age_group", "gender_presentation", "monk_skin_tone", "race_ethnicity_omb", "bmi_band", "height_band", "environment_category", "camera_shot", "speed", "camera_elevation", "camera_azimuth", "camera_distance", ] if hdf5_path.is_file() and ( hdf5_path.suffix == ".tar" or tarfile.is_tarfile(str(hdf5_path)) ): idx = 0 with tarfile.open(hdf5_path, "r") as tar: for member in tar.getmembers(): if not member.name.endswith(".h5"): continue video_path = member.name.lstrip("./").replace(".h5", "") if valid_paths is not None and video_path not in valid_paths: continue try: h5_file = tar.extractfile(member) if h5_file is None: continue import tempfile with tempfile.NamedTemporaryFile(suffix=".h5", delete=True) as tmp: tmp.write(h5_file.read()) tmp.flush() with h5py.File(tmp.name, "r") as f: frame_labels = f["label_indices"][:].tolist() video_metadata = metadata_df[metadata_df["path"] == video_path] if len(video_metadata) == 0: continue video_meta = video_metadata.iloc[0] example = { "path": video_path, "dataset": "of-syn", "frame_labels": frame_labels, } for field in metadata_fields: if field in video_meta and pd.notna(video_meta[field]): example[field] = str(video_meta[field]) yield idx, example idx += 1 except Exception as e: warnings.warn(f"Failed to process {member.name}: {e}") continue else: hdf5_files = sorted(hdf5_path.glob("**/*.h5")) idx = 0 for h5_file_path in hdf5_files: relative_path = h5_file_path.relative_to(hdf5_path) video_path = str(relative_path.with_suffix("")) if valid_paths is not None and video_path not in valid_paths: continue try: with h5py.File(h5_file_path, "r") as f: frame_labels = f["label_indices"][:].tolist() video_metadata = metadata_df[metadata_df["path"] == video_path] if len(video_metadata) == 0: continue video_meta = video_metadata.iloc[0] example = { "path": video_path, "dataset": "of-syn", "frame_labels": frame_labels, } for field in metadata_fields: if field in video_meta and pd.notna(video_meta[field]): example[field] = str(video_meta[field]) yield idx, example idx += 1 except Exception as e: warnings.warn(f"Failed to process {h5_file_path}: {e}") continue def _gen_paths_only(self, split_file): """Load paths only from a split file.""" df = pd.read_csv(split_file) for idx, row in df.iterrows(): yield idx, {"path": row["path"]} def _row_to_example(self, row): """Convert a DataFrame row to a typed example dict. Only includes fields present in the row. HuggingFace's Features.encode_example() will ignore extra fields and fill missing optional fields. """ example = {"path": str(row["path"])} # Core temporal fields for field, dtype in [ ("label", int), ("start", float), ("end", float), ("subject", int), ("cam", int), ]: if field in row.index and pd.notna(row[field]): example[field] = dtype(row[field]) if "dataset" in row.index and pd.notna(row["dataset"]): example["dataset"] = str(row["dataset"]) # Demographic and scene metadata (present only for syn data) for field in [ "age_group", "gender_presentation", "monk_skin_tone", "race_ethnicity_omb", "bmi_band", "height_band", "environment_category", "camera_shot", "speed", "camera_elevation", "camera_azimuth", "camera_distance", ]: if field in row.index and pd.notna(row[field]): example[field] = str(row[field]) return example