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omnifall

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Duplicate from simplexsigil2/omnifall

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Co-authored-by: David Schneider <simplexsigil2@users.noreply.huggingface.co>

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  1. .gitattributes +60 -0
  2. .gitignore +3 -0
  3. CONFIGS.md +123 -0
  4. LABELS.md +53 -0
  5. README.md +0 -0
  6. STRUCTURE.md +73 -0
  7. annotation_remarks.webm +3 -0
  8. data_files/omnifall-synthetic_av1.tar +3 -0
  9. data_files/oops_video_mapping.csv +0 -0
  10. data_files/syn_frame_wise_labels.tar.zst +3 -0
  11. generate_parquet.py +647 -0
  12. labels/GMDCSA24.csv +459 -0
  13. labels/OOPS.csv +0 -0
  14. labels/caucafall.csv +259 -0
  15. labels/cmdfall.csv +0 -0
  16. labels/edf.csv +509 -0
  17. labels/label2id.csv +17 -0
  18. labels/le2i.csv +968 -0
  19. labels/mcfd.csv +1353 -0
  20. labels/occu.csv +485 -0
  21. labels/of-syn.csv +0 -0
  22. labels/up_fall.csv +0 -0
  23. omnifall_builder.py +1192 -0
  24. omnifall_dataset_examples.ipynb +3 -0
  25. parquet/OOPS/test-00000-of-00001.parquet +3 -0
  26. parquet/OOPS/train-00000-of-00001.parquet +3 -0
  27. parquet/OOPS/validation-00000-of-00001.parquet +3 -0
  28. parquet/caucafall-cs/test-00000-of-00001.parquet +3 -0
  29. parquet/caucafall-cs/train-00000-of-00001.parquet +3 -0
  30. parquet/caucafall-cs/validation-00000-of-00001.parquet +3 -0
  31. parquet/caucafall-cv/train-00000-of-00001.parquet +3 -0
  32. parquet/caucafall-to-all-cs/test-00000-of-00001.parquet +3 -0
  33. parquet/caucafall-to-all-cs/train-00000-of-00001.parquet +3 -0
  34. parquet/caucafall-to-all-cs/validation-00000-of-00001.parquet +3 -0
  35. parquet/caucafall-to-all-cv/test-00000-of-00001.parquet +3 -0
  36. parquet/caucafall-to-all-cv/train-00000-of-00001.parquet +3 -0
  37. parquet/caucafall/test-00000-of-00001.parquet +3 -0
  38. parquet/caucafall/train-00000-of-00001.parquet +3 -0
  39. parquet/caucafall/validation-00000-of-00001.parquet +3 -0
  40. parquet/cmdfall-cs/test-00000-of-00001.parquet +3 -0
  41. parquet/cmdfall-cs/train-00000-of-00001.parquet +3 -0
  42. parquet/cmdfall-cs/validation-00000-of-00001.parquet +3 -0
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  45. parquet/cmdfall-cv/validation-00000-of-00001.parquet +3 -0
  46. parquet/cmdfall-to-all-cs/test-00000-of-00001.parquet +3 -0
  47. parquet/cmdfall-to-all-cs/train-00000-of-00001.parquet +3 -0
  48. parquet/cmdfall-to-all-cs/validation-00000-of-00001.parquet +3 -0
  49. parquet/cmdfall-to-all-cv/test-00000-of-00001.parquet +3 -0
  50. parquet/cmdfall-to-all-cv/train-00000-of-00001.parquet +3 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.mds filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.npz filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tar filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.wasm filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.pcm filter=lfs diff=lfs merge=lfs -text
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+ *.sam filter=lfs diff=lfs merge=lfs -text
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+ *.raw filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - compressed
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+ *.aac filter=lfs diff=lfs merge=lfs -text
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+ *.flac filter=lfs diff=lfs merge=lfs -text
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+ *.mp3 filter=lfs diff=lfs merge=lfs -text
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+ *.ogg filter=lfs diff=lfs merge=lfs -text
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+ *.wav filter=lfs diff=lfs merge=lfs -text
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+ # Image files - uncompressed
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+ *.bmp filter=lfs diff=lfs merge=lfs -text
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+ *.gif filter=lfs diff=lfs merge=lfs -text
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.tiff filter=lfs diff=lfs merge=lfs -text
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+ # Image files - compressed
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
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+ # Video files - compressed
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ *.webm filter=lfs diff=lfs merge=lfs -text
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+ *.ipynb filter=lfs diff=lfs merge=lfs -text
.gitignore ADDED
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+ convert_oops_via_to_csv.py
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+ .claude
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+ __pycache__
CONFIGS.md ADDED
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+ # OmniFall Configuration Reference
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+
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+ All configurations are loaded via:
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+
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+ ```python
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+ from datasets import load_dataset
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+ ds = load_dataset("simplexsigil2/omnifall", "<config_name>")
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+ ```
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+
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+ ## Labels (no train/val/test splits)
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+
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+ | Config | Description |
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+ |---|---|
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+ | `labels` (default) | All staged + OOPS labels (52,618 segments, 7 columns) |
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+ | `labels-syn` | OF-Syn labels with demographic metadata (19,228 segments, 19 columns) |
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+ | `metadata-syn` | OF-Syn video-level metadata (12,000 videos) |
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+ | `framewise-syn` | OF-Syn frame-wise labels (81 labels per video) |
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+
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+ ## Same-Domain Configs
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+
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+ ### OF-Staged
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+
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+ | Config | Description |
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+ |---|---|
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+ | `of-sta-cs` | 8 staged datasets, cross-subject splits |
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+ | `of-sta-cv` | 8 staged datasets, cross-view splits |
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+
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+ ### OF-ItW
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+
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+ | Config | Description |
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+ |---|---|
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+ | `of-itw` | OOPS-Fall in-the-wild genuine accidents |
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+
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+ Video loading requires the `omnifall` companion package (`pip install omnifall`).
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+
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+ ### OF-Syn
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+
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+ | Config | Description |
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+ |---|---|
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+ | `of-syn` | Random 80/10/10 split |
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+ | `of-syn-cross-age` | Cross-age split (train: adults, test: children/elderly) |
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+ | `of-syn-cross-ethnicity` | Cross-ethnicity split |
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+ | `of-syn-cross-bmi` | Cross-BMI split (train: normal/underweight, test: obese) |
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+
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+ Video loading requires the `omnifall` companion package.
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+
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+ ## Cross-Domain Evaluation (to-all)
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+
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+ Train/val from a specific source, test on **all** datasets (staged test + ItW test + Syn random test). The `-cs`/`-cv` suffix controls how the staged portion of the test set is split.
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+
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+ ### Combined source configs
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+
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+ | Config | Train/val source | Test |
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+ |---|---|---|
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+ | `of-sta-to-all-cs` | All 8 staged (CS) | All datasets (staged CS + ItW + Syn) |
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+ | `of-sta-to-all-cv` | All 8 staged (CV) | All datasets (staged CV + ItW + Syn) |
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+ | `of-syn-to-all-cs` | OF-Syn random | All datasets (staged CS + ItW + Syn) |
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+ | `of-syn-to-all-cv` | OF-Syn random | All datasets (staged CV + ItW + Syn) |
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+ | `of-sta-syn-to-all-cs` | Staged CS + OF-Syn | All datasets (staged CS + ItW + Syn) |
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+ | `of-sta-syn-to-all-cv` | Staged CV + OF-Syn | All datasets (staged CV + ItW + Syn) |
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+
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+ ### Individual staged dataset configs
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+
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+ | Config pattern | Train/val source |
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+ |---|---|
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+ | `{dataset}-to-all-cs` | Individual staged dataset (CS split) |
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+ | `{dataset}-to-all-cv` | Individual staged dataset (CV split) |
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+
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+ Available for: `caucafall`, `cmdfall`, `edf`, `gmdcsa24`, `le2i`, `mcfd`, `occu`, `up_fall`.
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+
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+ Note: `mcfd-to-all-cs` has train only (1 subject, CS val is empty). `caucafall-to-all-cv` has train only (1 room, CV val is empty).
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+
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+ ## Aggregate Configs (staged + OOPS combined)
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+
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+ | Config | Description |
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+ |---|---|
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+ | `cs` | Cross-subject splits for all staged + OOPS |
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+ | `cv` | Cross-view splits for all staged + OOPS |
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+
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+ ## Individual Dataset Configs
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+
82
+ Each staged dataset with both cross-subject (`-cs`) and cross-view (`-cv`) splits:
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+
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+ | CS | CV |
85
+ |---|---|
86
+ | `caucafall-cs` | `caucafall-cv` |
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+ | `cmdfall-cs` | `cmdfall-cv` |
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+ | `edf-cs` | `edf-cv` |
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+ | `gmdcsa24-cs` | `gmdcsa24-cv` |
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+ | `le2i-cs` | `le2i-cv` |
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+ | `mcfd-cs` | `mcfd-cv` |
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+ | `occu-cs` | `occu-cv` |
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+ | `up_fall-cs` | `up_fall-cv` |
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+
95
+ Note: `mcfd-cs` has train only (1 subject). `caucafall-cv` has train only (1 room).
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+
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+ ## Deprecated Config Names
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+
99
+ The following old config names still work but may be removed in a future version:
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+
101
+ | Old Name | Use Instead |
102
+ |---|---|
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+ | `cs-staged` | `of-sta-cs` |
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+ | `cv-staged` | `of-sta-cv` |
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+ | `cs-staged-wild` | `of-sta-to-all-cs` |
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+ | `cv-staged-wild` | `of-sta-to-all-cv` |
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+ | `of-sta-to-itw-cs` | `of-sta-to-all-cs` |
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+ | `of-sta-to-itw-cv` | `of-sta-to-all-cv` |
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+ | `of-sta-itw-cs` | `of-sta-to-all-cs` |
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+ | `of-sta-itw-cv` | `of-sta-to-all-cv` |
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+ | `of-syn-to-itw` | `of-syn-to-all-cs` |
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+ | `of-syn-itw` | `of-syn-to-all-cs` |
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+ | `of-sta-syn-to-itw-cs` | `of-sta-syn-to-all-cs` |
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+ | `of-sta-syn-to-itw-cv` | `of-sta-syn-to-all-cv` |
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+ | `OOPS` | `of-itw` |
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+ | `caucafall` | `caucafall-cs` |
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+ | `cmdfall` | `cmdfall-cs` |
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+ | `edf` | `edf-cs` |
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+ | `gmdcsa24` | `gmdcsa24-cs` |
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+ | `le2i` | `le2i-cs` |
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+ | `mcfd` | `mcfd-cs` |
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+ | `occu` | `occu-cs` |
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+ | `up_fall` | `up_fall-cs` |
LABELS.md ADDED
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+ # OmniFall Label Definitions
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+
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+ The benchmark uses a sixteen-class activity taxonomy. Staged datasets use classes 0-9. OF-ItW and OF-Syn use the full range 0-15. The extended classes (10-15) are infrequent and can be mapped to "other" (class 9) for compatibility.
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+
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+ ## Core Classes (0-9, all datasets)
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+
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+ - **`0|walk`** - Move around, including jogging and running and "drunk walking", but only if it is not part of some special exercise like pulling your knees up. Not when pushing a large object like a chair, but included carrying something small like an apple.
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+ - **`1|fall`** - The act of falling (from any previous state). Includes falling on a bed, if the process is not a controlled lying down with arms as support.
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+ - **`2|fallen`** - Being on the ground or a mattress after a fall.
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+ - **`3|sit_down`** - Sitting down on bed or chair or ground.
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+ - **`4|sitting`** - Sitting on bed or chair or ground.
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+ - **`5|lie_down`** - Lying down intentionally (in contrast to a fall) on ground or bed.
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+ - **`6|lying`** - Being in a lying position (in bed or on the ground) after intentionally getting into that position.
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+ - **`7|stand_up`** - Standing up from a fallen state, from lying or sitting. Includes getting from lying position into sitting position.
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+ - **`8|standing`** - Standing around without walking.
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+ - **`9|other`** - Any other activity, including e.g. walking while pushing an object like a chair.
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+
18
+ ## Extended Classes (10-15, OF-ItW and OF-Syn)
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+
20
+ These classes capture additional activities observed in genuine accident and synthetic videos. They do not occur in staged datasets.
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+
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+ - **`10|kneel_down`** - Transitioning from standing or another posture to a kneeling position.
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+ - **`11|kneeling`** - Being in a kneeling position (static posture).
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+ - **`12|squat_down`** - Transitioning to a squatting position.
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+ - **`13|squatting`** - Being in a squatting position (static posture).
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+ - **`14|crawl`** - Crawling on hands and knees or similar locomotion on the ground.
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+ - **`15|jump`** - Jumping action, including vertical jumps and jumps from elevated positions.
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+
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+ ## Motion Types
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+
31
+ There are two types of motions: **dynamic** ones like `walk` or `stand_up` and **static** ones like `fallen`, `sitting`, `lying`.
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+
33
+ **Dynamic motions** are annotated starting from the first frame that belongs to that action. For example, the transition from `walk` to `fall` begins at the first frame where the motion visibly diverges from walking.
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+
35
+ **Static motions** begin at the first frame where the person reaches a resting state. For `sit_down`, the label ends when the person stops adjusting their body position. For `fall`, the label ends when inertia-driven movement stops, and the subsequent `fallen` segment may still contain ground-level movement unrelated to the fall or recovery.
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+
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+ ## Label Sequences
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+
39
+ There are natural sequences of labels like `fall`, `fallen`, `stand_up`. However, these do not always appear together. A person might stand up directly after falling without a `fallen` segment, or sit down and immediately stand up without a `sitting` segment.
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+
41
+ Lying down is intentional (in contrast to `fall`). Falls can follow `sit_down` or `lie_down` when falling from a chair or bed.
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+
43
+ Getting from a lying position to sitting counts as `stand_up`, even if the person remains seated. A full sequence might be: `lying`, `stand_up`, `sitting`, `stand_up` (lying in bed, sitting up, pausing, then fully standing).
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+
45
+ The distinction between `sit_down` followed by `lie_down` versus a single `lie_down` depends on whether there is a moment of rest in the sitting position. If the person transitions directly from standing through sitting to lying without pause, only `lie_down` is labeled.
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+
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+ ## Annotation Process
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+
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+ The annotation video below demonstrates how VGG VIA was used for dense temporal annotation. Pre-existing dataset labels served as visual aid, but all videos were relabeled using OmniFall's label definitions to ensure consistency. The example shows CMDFall, which already provides relatively detailed labels; other source datasets have sparser original annotations.
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+
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+ Blurred regions in the video were added in post-processing to protect subject privacy on this page.
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+
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+ <video src="https://huggingface.co/datasets/simplexsigil2/omnifall/resolve/main/annotation_remarks.webm" controls />
README.md ADDED
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STRUCTURE.md ADDED
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+ # OmniFall Repository Structure
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+
3
+ ## Directory Layout
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+
5
+ ```
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+ parquet/ Pre-built parquet files for all configs (used by load_dataset)
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+ labels/ CSV files containing temporal segment annotations
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+ caucafall.csv ... up_fall.csv Staged dataset labels (7 columns)
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+ OOPS.csv OF-ItW labels (7 columns)
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+ of-syn.csv OF-Syn labels (19 columns: 7 core + 12 demographic)
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+ splits/
12
+ cs/ Cross-subject splits (staged + OOPS)
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+ cv/ Cross-view splits (staged + OOPS)
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+ syn/ Synthetic splits (random, cross_age, cross_ethnicity, cross_bmi)
15
+ videos/metadata.csv OF-Syn video-level metadata (12,000 videos)
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+ data_files/
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+ omnifall-synthetic_av1.tar OF-Syn video archive (12,000 AV1-encoded MP4s)
18
+ syn_frame_wise_labels.tar.zst OF-Syn frame-wise HDF5 labels
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+ oops_video_mapping.csv Mapping from OOPS original filenames to OF-ItW names
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+ prepare_oops_videos.py Script to extract OOPS videos for OF-ItW
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+ generate_parquet.py Script to regenerate parquet files from labels + splits
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+ ```
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+
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+ ## Label Format
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+
26
+ Each label file in `labels/` contains temporal segment annotations:
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+
28
+ ```csv
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+ path,label,start,end,subject,cam,dataset
30
+ path/to/clip,class_id,start_time,end_time,subject_id,camera_id,dataset_name
31
+ ```
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+
33
+ | Column | Type | Description |
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+ |---|---|---|
35
+ | `path` | string | Relative path to the video (dataset-specific root) |
36
+ | `label` | int | Class ID (0-15), see [LABELS.md](LABELS.md) |
37
+ | `start` | float | Segment start time in seconds |
38
+ | `end` | float | Segment end time in seconds |
39
+ | `subject` | int | Subject ID (-1 for OF-ItW and OF-Syn) |
40
+ | `cam` | int | Camera view ID (-1 for OF-ItW and OF-Syn) |
41
+ | `dataset` | string | Source dataset name |
42
+
43
+ ### OF-Syn Extended Labels
44
+
45
+ `labels/of-syn.csv` contains 12 additional demographic and scene metadata columns:
46
+
47
+ | Column | Values |
48
+ |---|---|
49
+ | `age_group` | toddlers_1_4, children_5_12, teenagers_13_17, young_adults_18_34, middle_aged_35_64, elderly_65_plus |
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+ | `gender_presentation` | male, female |
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+ | `monk_skin_tone` | mst1 through mst10 |
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+ | `race_ethnicity_omb` | white, black, asian, hispanic_latino, aian, nhpi, mena |
53
+ | `bmi_band` | underweight, normal, overweight, obese |
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+ | `height_band` | short, avg, tall |
55
+ | `environment_category` | indoor, outdoor |
56
+ | `camera_shot` | static_wide, static_medium_wide |
57
+ | `speed` | 24fps_rt, 25fps_rt, 30fps_rt, std_rt |
58
+ | `camera_elevation` | eye, low, high, top |
59
+ | `camera_azimuth` | front, rear, left, right |
60
+ | `camera_distance` | medium, far |
61
+
62
+ These fields are available in OF-Syn configs (`of-syn`, `of-syn-cross-*`, `labels-syn`, `metadata-syn`, `framewise-syn`). Cross-domain configs that mix sources use only the 7 core columns.
63
+
64
+ ## Split Format
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+
66
+ Split files in `splits/` list video paths included in each partition:
67
+
68
+ ```csv
69
+ path
70
+ path/to/clip
71
+ ```
72
+
73
+ Each video path maps to one or more temporal segments in the corresponding label file. The parquet configs contain the pre-merged result of joining split paths with labels.
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@@ -0,0 +1,3 @@
 
 
 
 
1
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1
+ """One-time script to generate parquet files for all OmniFall HF configs.
2
+
3
+ Created for the HF datasets 4.6 migration (dataset scripts no longer supported).
4
+ Generates parquet files that enable native load_dataset() without custom builder code.
5
+ Can be safely deleted after parquet files are committed to the Hub.
6
+
7
+ Usage:
8
+ python generate_parquet.py
9
+ """
10
+
11
+ import os
12
+ import subprocess
13
+ import tarfile
14
+ import tempfile
15
+ from pathlib import Path
16
+
17
+ import numpy as np
18
+ import pandas as pd
19
+
20
+ REPO_ROOT = Path(__file__).parent
21
+ PARQUET_DIR = REPO_ROOT / "parquet"
22
+
23
+ # ---- Label and split file paths ----
24
+
25
+ STAGED_DATASETS = [
26
+ "caucafall", "cmdfall", "edf", "gmdcsa24",
27
+ "le2i", "mcfd", "occu", "up_fall",
28
+ ]
29
+
30
+ # Label CSV filenames (note: GMDCSA24 has capitalized filename)
31
+ STAGED_LABEL_FILES = {
32
+ "caucafall": "labels/caucafall.csv",
33
+ "cmdfall": "labels/cmdfall.csv",
34
+ "edf": "labels/edf.csv",
35
+ "gmdcsa24": "labels/GMDCSA24.csv",
36
+ "le2i": "labels/le2i.csv",
37
+ "mcfd": "labels/mcfd.csv",
38
+ "occu": "labels/occu.csv",
39
+ "up_fall": "labels/up_fall.csv",
40
+ }
41
+ ITW_LABEL_FILE = "labels/OOPS.csv"
42
+ SYN_LABEL_FILE = "labels/of-syn.csv"
43
+ METADATA_FILE = "videos/metadata.csv"
44
+
45
+ CORE_COLUMNS = ["path", "label", "start", "end", "subject", "cam", "dataset"]
46
+ DEMOGRAPHIC_COLUMNS = [
47
+ "age_group", "gender_presentation", "monk_skin_tone",
48
+ "race_ethnicity_omb", "bmi_band", "height_band",
49
+ "environment_category", "camera_shot", "speed",
50
+ "camera_elevation", "camera_azimuth", "camera_distance",
51
+ ]
52
+ SYN_COLUMNS = CORE_COLUMNS + DEMOGRAPHIC_COLUMNS
53
+ METADATA_COLUMNS = ["path", "dataset"] + DEMOGRAPHIC_COLUMNS
54
+
55
+ # ---- Deprecated aliases ----
56
+
57
+ DEPRECATED_ALIASES = {
58
+ "cs-staged": "of-sta-cs",
59
+ "cv-staged": "of-sta-cv",
60
+ "OOPS": "of-itw",
61
+ # Old unsuffixed individual dataset names (were CS-only)
62
+ "caucafall": "caucafall-cs",
63
+ "cmdfall": "cmdfall-cs",
64
+ "edf": "edf-cs",
65
+ "gmdcsa24": "gmdcsa24-cs",
66
+ "le2i": "le2i-cs",
67
+ "mcfd": "mcfd-cs",
68
+ "occu": "occu-cs",
69
+ "up_fall": "up_fall-cs",
70
+ # Old cross-domain "-to-itw" names (now subsumed by "-to-all")
71
+ "of-sta-to-itw-cs": "of-sta-to-all-cs",
72
+ "of-sta-to-itw-cv": "of-sta-to-all-cv",
73
+ "of-syn-to-itw": "of-syn-to-all-cs",
74
+ "of-sta-syn-to-itw-cs": "of-sta-syn-to-all-cs",
75
+ "of-sta-syn-to-itw-cv": "of-sta-syn-to-all-cv",
76
+ "of-sta-itw-cs": "of-sta-to-all-cs",
77
+ "of-sta-itw-cv": "of-sta-to-all-cv",
78
+ "of-syn-itw": "of-syn-to-all-cs",
79
+ "cs-staged-wild": "of-sta-to-all-cs",
80
+ "cv-staged-wild": "of-sta-to-all-cv",
81
+ }
82
+
83
+
84
+ # ---- Helpers ----
85
+
86
+ def load_csv(relpath):
87
+ """Load a CSV file relative to REPO_ROOT."""
88
+ return pd.read_csv(REPO_ROOT / relpath)
89
+
90
+
91
+ def load_staged_labels(datasets=None):
92
+ """Load and concatenate staged label CSVs."""
93
+ if datasets is None:
94
+ datasets = STAGED_DATASETS
95
+ dfs = [load_csv(STAGED_LABEL_FILES[ds]) for ds in datasets]
96
+ return pd.concat(dfs, ignore_index=True)
97
+
98
+
99
+ def load_itw_labels():
100
+ """Load OOPS/ItW labels."""
101
+ return load_csv(ITW_LABEL_FILE)
102
+
103
+
104
+ def load_syn_labels():
105
+ """Load OF-Syn labels (19-col)."""
106
+ return load_csv(SYN_LABEL_FILE)
107
+
108
+
109
+ def staged_split_files(split_type, split_name):
110
+ """Return list of split CSV relative paths for all 8 staged datasets."""
111
+ return [f"splits/{split_type}/{ds}/{split_name}.csv" for ds in STAGED_DATASETS]
112
+
113
+
114
+ def merge_split_labels(split_files, labels_df):
115
+ """Merge split paths with labels, replicating _gen_split_merge logic."""
116
+ split_dfs = [load_csv(sf) for sf in split_files]
117
+ split_df = pd.concat(split_dfs, ignore_index=True)
118
+ merged = pd.merge(split_df, labels_df, on="path", how="left")
119
+ # Drop rows where the path didn't match any label (orphaned split entries)
120
+ unmatched = merged["label"].isna()
121
+ if unmatched.any():
122
+ n = unmatched.sum()
123
+ paths = merged.loc[unmatched, "path"].tolist()
124
+ print(f" WARNING: Dropping {n} unmatched path(s): {paths}")
125
+ merged = merged[~unmatched].reset_index(drop=True)
126
+ return merged
127
+
128
+
129
+ def cast_core_dtypes(df):
130
+ """Cast core columns to correct dtypes for parquet/ClassLabel."""
131
+ df = df.copy()
132
+ df["path"] = df["path"].astype(str)
133
+ df["label"] = df["label"].astype(int)
134
+ df["start"] = df["start"].astype(np.float32)
135
+ df["end"] = df["end"].astype(np.float32)
136
+ df["subject"] = df["subject"].astype(np.int32)
137
+ df["cam"] = df["cam"].astype(np.int32)
138
+ df["dataset"] = df["dataset"].astype(str)
139
+ return df
140
+
141
+
142
+ def cast_demographic_dtypes(df):
143
+ """Cast demographic columns to string (for ClassLabel encoding)."""
144
+ df = df.copy()
145
+ for col in DEMOGRAPHIC_COLUMNS:
146
+ if col in df.columns:
147
+ df[col] = df[col].astype(str)
148
+ return df
149
+
150
+
151
+ def select_and_cast(df, columns, schema="core"):
152
+ """Select columns and cast dtypes."""
153
+ df = df[columns].copy()
154
+ if schema in ("core", "syn"):
155
+ df = cast_core_dtypes(df)
156
+ if schema in ("syn", "metadata"):
157
+ df = cast_demographic_dtypes(df)
158
+ return df
159
+
160
+
161
+ def write_parquet(df, config_name, split_name):
162
+ """Write a dataframe as a parquet file in the expected layout.
163
+
164
+ Returns the output path, or None if the dataframe is empty (Arrow can't
165
+ handle 0-row parquet files).
166
+ """
167
+ if len(df) == 0:
168
+ print(f" SKIP {config_name}/{split_name}: 0 rows (not written)")
169
+ return None
170
+ out_dir = PARQUET_DIR / config_name
171
+ out_dir.mkdir(parents=True, exist_ok=True)
172
+ out_path = out_dir / f"{split_name}-00000-of-00001.parquet"
173
+ df.to_parquet(out_path, index=False)
174
+ return out_path
175
+
176
+
177
+ def generate_split_config(config_name, split_type, split_files_fn, labels_df, columns,
178
+ schema="core"):
179
+ """Generate train/val/test parquet files for a split-based config."""
180
+ results = {}
181
+ for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
182
+ sf = split_files_fn(split_type, csv_name)
183
+ merged = merge_split_labels(sf, labels_df)
184
+ df = select_and_cast(merged, columns, schema)
185
+ path = write_parquet(df, config_name, split_name)
186
+ results[split_name] = len(df)
187
+ return results
188
+
189
+
190
+ def copy_parquet(source_config, target_config):
191
+ """Copy parquet files from source config to target config (for deprecated aliases)."""
192
+ src_dir = PARQUET_DIR / source_config
193
+ dst_dir = PARQUET_DIR / target_config
194
+ dst_dir.mkdir(parents=True, exist_ok=True)
195
+ results = {}
196
+ for src_file in sorted(src_dir.glob("*.parquet")):
197
+ dst_file = dst_dir / src_file.name
198
+ # Read and re-write to avoid symlink issues with git
199
+ df = pd.read_parquet(src_file)
200
+ df.to_parquet(dst_file, index=False)
201
+ split_name = src_file.stem.split("-")[0]
202
+ results[split_name] = len(df)
203
+ return results
204
+
205
+
206
+ # ---- Config generators ----
207
+
208
+ def gen_labels():
209
+ """Config: labels - All staged + OOPS labels, single train split."""
210
+ staged = load_staged_labels()
211
+ itw = load_itw_labels()
212
+ df = pd.concat([staged, itw], ignore_index=True)
213
+ df = select_and_cast(df, CORE_COLUMNS, "core")
214
+ path = write_parquet(df, "labels", "train")
215
+ return {"labels": {"train": len(df)}}
216
+
217
+
218
+ def gen_labels_syn():
219
+ """Config: labels-syn - OF-Syn labels with demographics, single train split."""
220
+ df = load_syn_labels()
221
+ df = select_and_cast(df, SYN_COLUMNS, "syn")
222
+ path = write_parquet(df, "labels-syn", "train")
223
+ return {"labels-syn": {"train": len(df)}}
224
+
225
+
226
+ def gen_metadata_syn():
227
+ """Config: metadata-syn - OF-Syn video-level metadata, single train split."""
228
+ df = load_csv(METADATA_FILE)
229
+ # Select only the metadata columns (drop prompt_id)
230
+ metadata_cols = ["path"] + DEMOGRAPHIC_COLUMNS
231
+ available = [c for c in metadata_cols if c in df.columns]
232
+ df = df[available].drop_duplicates(subset=["path"]).reset_index(drop=True)
233
+ df["dataset"] = "of-syn"
234
+ df = select_and_cast(df, METADATA_COLUMNS, "metadata")
235
+ path = write_parquet(df, "metadata-syn", "train")
236
+ return {"metadata-syn": {"train": len(df)}}
237
+
238
+
239
+ def gen_of_sta(split_type):
240
+ """Config: of-sta-cs / of-sta-cv - 8 staged datasets combined."""
241
+ config_name = f"of-sta-{split_type}"
242
+ labels = load_staged_labels()
243
+ results = generate_split_config(
244
+ config_name, split_type,
245
+ lambda st, sn: staged_split_files(st, sn),
246
+ labels, CORE_COLUMNS, "core",
247
+ )
248
+ return {config_name: results}
249
+
250
+
251
+ def gen_of_itw():
252
+ """Config: of-itw - OOPS-Fall in-the-wild."""
253
+ labels = load_itw_labels()
254
+ results = {}
255
+ for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
256
+ sf = [f"splits/cs/OOPS/{csv_name}.csv"]
257
+ merged = merge_split_labels(sf, labels)
258
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
259
+ write_parquet(df, "of-itw", split_name)
260
+ results[split_name] = len(df)
261
+ return {"of-itw": results}
262
+
263
+
264
+ def gen_of_syn(split_type, config_name):
265
+ """Config: of-syn variants."""
266
+ labels = load_syn_labels()
267
+ results = {}
268
+ for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
269
+ sf = [f"splits/syn/{split_type}/{csv_name}.csv"]
270
+ merged = merge_split_labels(sf, labels)
271
+ df = select_and_cast(merged, SYN_COLUMNS, "syn")
272
+ write_parquet(df, config_name, split_name)
273
+ results[split_name] = len(df)
274
+ return {config_name: results}
275
+
276
+
277
+ def gen_crossdomain(config_name, train_split_type, train_source, test_split_type,
278
+ test_source):
279
+ """Config: cross-domain configs (train from one source, test from another)."""
280
+ # Load labels for train and test sources
281
+ if train_source == "staged":
282
+ train_labels = load_staged_labels()
283
+ train_split_fn = lambda sn: staged_split_files(train_split_type, sn)
284
+ elif train_source == "syn":
285
+ train_labels = load_syn_labels()
286
+ train_split_fn = lambda sn: [f"splits/syn/{train_split_type}/{sn}.csv"]
287
+ else:
288
+ raise ValueError(f"Unknown train_source: {train_source}")
289
+
290
+ if test_source == "itw":
291
+ test_labels = load_itw_labels()
292
+ test_split_fn = lambda sn: [f"splits/{test_split_type}/OOPS/{sn}.csv"]
293
+ else:
294
+ raise ValueError(f"Unknown test_source: {test_source}")
295
+
296
+ results = {}
297
+
298
+ # Train and val come from train source
299
+ for split_name, csv_name in [("train", "train"), ("validation", "val")]:
300
+ sf = train_split_fn(csv_name)
301
+ merged = merge_split_labels(sf, train_labels)
302
+ # Cross-domain always uses core 7-col schema
303
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
304
+ write_parquet(df, config_name, split_name)
305
+ results[split_name] = len(df)
306
+
307
+ # Test comes from test source
308
+ sf = test_split_fn("test")
309
+ merged = merge_split_labels(sf, test_labels)
310
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
311
+ write_parquet(df, config_name, "test")
312
+ results["test"] = len(df)
313
+
314
+ return {config_name: results}
315
+
316
+
317
+ def gen_crossdomain_staged_syn(config_name, staged_split_type, syn_split_type,
318
+ test_split_type):
319
+ """Config: train/val from staged + syn combined, test from OOPS."""
320
+ staged_labels = load_staged_labels()
321
+ syn_labels = load_syn_labels()
322
+ itw_labels = load_itw_labels()
323
+
324
+ results = {}
325
+
326
+ for split_name, csv_name in [("train", "train"), ("validation", "val")]:
327
+ # Staged part
328
+ sta_sf = staged_split_files(staged_split_type, csv_name)
329
+ sta_merged = merge_split_labels(sta_sf, staged_labels)
330
+ sta_df = select_and_cast(sta_merged, CORE_COLUMNS, "core")
331
+
332
+ # Syn part
333
+ syn_sf = [f"splits/syn/{syn_split_type}/{csv_name}.csv"]
334
+ syn_merged = merge_split_labels(syn_sf, syn_labels)
335
+ syn_df = select_and_cast(syn_merged, CORE_COLUMNS, "core")
336
+
337
+ df = pd.concat([sta_df, syn_df], ignore_index=True)
338
+ write_parquet(df, config_name, split_name)
339
+ results[split_name] = len(df)
340
+
341
+ # Test from OOPS
342
+ sf = [f"splits/{test_split_type}/OOPS/test.csv"]
343
+ merged = merge_split_labels(sf, itw_labels)
344
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
345
+ write_parquet(df, config_name, "test")
346
+ results["test"] = len(df)
347
+
348
+ return {config_name: results}
349
+
350
+
351
+ def gen_to_all(config_name, staged_split_type, train_source, ds_name=None):
352
+ """Config: train/val from specified source, test from ALL datasets.
353
+
354
+ Test set is always: staged test (cs or cv) + itw test + syn random test.
355
+ Train/val come from the specified source:
356
+ - "individual": single staged dataset (needs ds_name)
357
+ - "staged": all 8 staged datasets
358
+ - "syn": synthetic random split
359
+ - "staged+syn": staged + synthetic combined
360
+ """
361
+ staged_labels = load_staged_labels()
362
+ itw_labels = load_itw_labels()
363
+ syn_labels = load_syn_labels()
364
+
365
+ results = {}
366
+
367
+ # --- Train/val from source ---
368
+ for split_name, csv_name in [("train", "train"), ("validation", "val")]:
369
+ if train_source == "individual":
370
+ sf = [f"splits/{staged_split_type}/{ds_name}/{csv_name}.csv"]
371
+ merged = merge_split_labels(sf, load_csv(STAGED_LABEL_FILES[ds_name]))
372
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
373
+ elif train_source == "staged":
374
+ sf = staged_split_files(staged_split_type, csv_name)
375
+ merged = merge_split_labels(sf, staged_labels)
376
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
377
+ elif train_source == "syn":
378
+ sf = [f"splits/syn/random/{csv_name}.csv"]
379
+ merged = merge_split_labels(sf, syn_labels)
380
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
381
+ elif train_source == "staged+syn":
382
+ sta_sf = staged_split_files(staged_split_type, csv_name)
383
+ sta_merged = merge_split_labels(sta_sf, staged_labels)
384
+ sta_df = select_and_cast(sta_merged, CORE_COLUMNS, "core")
385
+ syn_sf = [f"splits/syn/random/{csv_name}.csv"]
386
+ syn_merged = merge_split_labels(syn_sf, syn_labels)
387
+ syn_df = select_and_cast(syn_merged, CORE_COLUMNS, "core")
388
+ df = pd.concat([sta_df, syn_df], ignore_index=True)
389
+ else:
390
+ raise ValueError(f"Unknown train_source: {train_source}")
391
+
392
+ write_parquet(df, config_name, split_name)
393
+ results[split_name] = len(df)
394
+
395
+ # --- Test from ALL datasets ---
396
+ # Staged test (cs or cv)
397
+ sta_sf = staged_split_files(staged_split_type, "test")
398
+ sta_merged = merge_split_labels(sta_sf, staged_labels)
399
+ sta_test = select_and_cast(sta_merged, CORE_COLUMNS, "core")
400
+
401
+ # ItW test (OOPS, always uses same split file under cs/ dir)
402
+ itw_sf = [f"splits/{staged_split_type}/OOPS/test.csv"]
403
+ itw_merged = merge_split_labels(itw_sf, itw_labels)
404
+ itw_test = select_and_cast(itw_merged, CORE_COLUMNS, "core")
405
+
406
+ # Syn test (always random split)
407
+ syn_sf = ["splits/syn/random/test.csv"]
408
+ syn_merged = merge_split_labels(syn_sf, syn_labels)
409
+ syn_test = select_and_cast(syn_merged, CORE_COLUMNS, "core")
410
+
411
+ test_df = pd.concat([sta_test, itw_test, syn_test], ignore_index=True)
412
+ write_parquet(test_df, config_name, "test")
413
+ results["test"] = len(test_df)
414
+
415
+ return {config_name: results}
416
+
417
+
418
+ def gen_aggregate(split_type):
419
+ """Config: cs / cv - all staged + OOPS combined."""
420
+ config_name = split_type
421
+ all_labels = pd.concat([load_staged_labels(), load_itw_labels()], ignore_index=True)
422
+ results = {}
423
+ for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
424
+ sf = staged_split_files(split_type, csv_name) + [
425
+ f"splits/{split_type}/OOPS/{csv_name}.csv"
426
+ ]
427
+ merged = merge_split_labels(sf, all_labels)
428
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
429
+ write_parquet(df, config_name, split_name)
430
+ results[split_name] = len(df)
431
+ return {config_name: results}
432
+
433
+
434
+ def gen_individual(ds_name, split_type):
435
+ """Config: individual dataset with explicit split type (cs or cv)."""
436
+ config_name = f"{ds_name}-{split_type}"
437
+ labels = load_csv(STAGED_LABEL_FILES[ds_name])
438
+ results = {}
439
+ for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
440
+ sf = [f"splits/{split_type}/{ds_name}/{csv_name}.csv"]
441
+ merged = merge_split_labels(sf, labels)
442
+ df = select_and_cast(merged, CORE_COLUMNS, "core")
443
+ write_parquet(df, config_name, split_name)
444
+ results[split_name] = len(df)
445
+ return {config_name: results}
446
+
447
+
448
+ def gen_framewise_syn():
449
+ """Config: framewise-syn - OF-Syn frame-wise labels from HDF5, single train split.
450
+
451
+ Reads syn_frame_wise_labels.tar.zst (HDF5 files with 81 frame labels each),
452
+ merges with videos/metadata.csv for demographics, writes a single parquet.
453
+ Requires h5py at generation time only.
454
+ """
455
+ import h5py
456
+
457
+ archive_path = REPO_ROOT / "data_files" / "syn_frame_wise_labels.tar.zst"
458
+ if not archive_path.exists():
459
+ print(f" SKIP framewise-syn: archive not found at {archive_path}")
460
+ return {"framewise-syn": {}}
461
+
462
+ metadata_df = load_csv(METADATA_FILE)
463
+
464
+ metadata_fields = DEMOGRAPHIC_COLUMNS
465
+
466
+ rows = []
467
+
468
+ with tempfile.TemporaryDirectory() as tmpdir:
469
+ # Extract the .tar.zst archive (tarfile doesn't support zstd natively)
470
+ subprocess.run(
471
+ ["tar", "--zstd", "-xf", str(archive_path), "-C", tmpdir],
472
+ check=True,
473
+ )
474
+ tmppath = Path(tmpdir)
475
+ h5_files = sorted(tmppath.glob("**/*.h5"))
476
+ print(f" Found {len(h5_files)} HDF5 files in archive")
477
+
478
+ for h5_file_path in h5_files:
479
+ relative_path = h5_file_path.relative_to(tmppath)
480
+ video_path = str(relative_path.with_suffix(""))
481
+
482
+ try:
483
+ with h5py.File(h5_file_path, "r") as f:
484
+ frame_labels = f["label_indices"][:].tolist()
485
+ except Exception as e:
486
+ print(f" WARNING: Failed to read {h5_file_path}: {e}")
487
+ continue
488
+
489
+ video_metadata = metadata_df[metadata_df["path"] == video_path]
490
+ if len(video_metadata) == 0:
491
+ print(f" WARNING: No metadata for {video_path}, skipping")
492
+ continue
493
+ video_meta = video_metadata.iloc[0]
494
+
495
+ row = {
496
+ "path": video_path,
497
+ "dataset": "of-syn",
498
+ "frame_labels": frame_labels,
499
+ }
500
+ for field in metadata_fields:
501
+ if field in video_meta and pd.notna(video_meta[field]):
502
+ row[field] = str(video_meta[field])
503
+ else:
504
+ row[field] = ""
505
+ rows.append(row)
506
+
507
+ df = pd.DataFrame(rows)
508
+ # Cast demographic columns to string
509
+ df = cast_demographic_dtypes(df)
510
+ df["path"] = df["path"].astype(str)
511
+ df["dataset"] = df["dataset"].astype(str)
512
+
513
+ path = write_parquet(df, "framewise-syn", "train")
514
+ return {"framewise-syn": {"train": len(df)}}
515
+
516
+
517
+ # ---- Main ----
518
+
519
+ def main():
520
+ print(f"Generating parquet files in: {PARQUET_DIR}")
521
+ PARQUET_DIR.mkdir(parents=True, exist_ok=True)
522
+
523
+ all_results = {}
524
+
525
+ # Labels configs (single train split)
526
+ print("\n--- Labels configs ---")
527
+ for gen_fn in [gen_labels, gen_labels_syn, gen_metadata_syn, gen_framewise_syn]:
528
+ result = gen_fn()
529
+ all_results.update(result)
530
+ for config, splits in result.items():
531
+ for split, count in splits.items():
532
+ print(f" {config}/{split}: {count} rows")
533
+
534
+ # OF-Staged configs
535
+ print("\n--- OF-Staged configs ---")
536
+ for st in ["cs", "cv"]:
537
+ result = gen_of_sta(st)
538
+ all_results.update(result)
539
+ for config, splits in result.items():
540
+ for split, count in splits.items():
541
+ print(f" {config}/{split}: {count} rows")
542
+
543
+ # OF-ItW config
544
+ print("\n--- OF-ItW config ---")
545
+ result = gen_of_itw()
546
+ all_results.update(result)
547
+ for config, splits in result.items():
548
+ for split, count in splits.items():
549
+ print(f" {config}/{split}: {count} rows")
550
+
551
+ # OF-Syn configs
552
+ print("\n--- OF-Syn configs ---")
553
+ syn_configs = [
554
+ ("random", "of-syn"),
555
+ ("cross_age", "of-syn-cross-age"),
556
+ ("cross_ethnicity", "of-syn-cross-ethnicity"),
557
+ ("cross_bmi", "of-syn-cross-bmi"),
558
+ ]
559
+ for split_type, config_name in syn_configs:
560
+ result = gen_of_syn(split_type, config_name)
561
+ all_results.update(result)
562
+ for config, splits in result.items():
563
+ for split, count in splits.items():
564
+ print(f" {config}/{split}: {count} rows")
565
+
566
+ # Cross-domain "to-all" configs (train/val from source, test from ALL)
567
+ print("\n--- Cross-domain to-all configs ---")
568
+
569
+ # Individual staged dataset to-all
570
+ for ds_name in STAGED_DATASETS:
571
+ for st in ["cs", "cv"]:
572
+ config_name = f"{ds_name}-to-all-{st}"
573
+ result = gen_to_all(config_name, st, "individual", ds_name=ds_name)
574
+ all_results.update(result)
575
+ for config, splits in result.items():
576
+ for split, count in splits.items():
577
+ print(f" {config}/{split}: {count} rows")
578
+
579
+ # All staged to-all
580
+ for st in ["cs", "cv"]:
581
+ config_name = f"of-sta-to-all-{st}"
582
+ result = gen_to_all(config_name, st, "staged")
583
+ all_results.update(result)
584
+ for config, splits in result.items():
585
+ for split, count in splits.items():
586
+ print(f" {config}/{split}: {count} rows")
587
+
588
+ # Syn to-all
589
+ for st in ["cs", "cv"]:
590
+ config_name = f"of-syn-to-all-{st}"
591
+ result = gen_to_all(config_name, st, "syn")
592
+ all_results.update(result)
593
+ for config, splits in result.items():
594
+ for split, count in splits.items():
595
+ print(f" {config}/{split}: {count} rows")
596
+
597
+ # Staged + syn to-all
598
+ for st in ["cs", "cv"]:
599
+ config_name = f"of-sta-syn-to-all-{st}"
600
+ result = gen_to_all(config_name, st, "staged+syn")
601
+ all_results.update(result)
602
+ for config, splits in result.items():
603
+ for split, count in splits.items():
604
+ print(f" {config}/{split}: {count} rows")
605
+
606
+ # Aggregate configs
607
+ print("\n--- Aggregate configs ---")
608
+ for st in ["cs", "cv"]:
609
+ result = gen_aggregate(st)
610
+ all_results.update(result)
611
+ for config, splits in result.items():
612
+ for split, count in splits.items():
613
+ print(f" {config}/{split}: {count} rows")
614
+
615
+ # Individual dataset configs (both CS and CV)
616
+ print("\n--- Individual dataset configs ---")
617
+ for ds_name in STAGED_DATASETS:
618
+ for st in ["cs", "cv"]:
619
+ result = gen_individual(ds_name, st)
620
+ all_results.update(result)
621
+ for config, splits in result.items():
622
+ for split, count in splits.items():
623
+ print(f" {config}/{split}: {count} rows")
624
+
625
+ # Deprecated aliases (copy parquet files)
626
+ print("\n--- Deprecated aliases ---")
627
+ for old_name, new_name in DEPRECATED_ALIASES.items():
628
+ result = copy_parquet(new_name, old_name)
629
+ for split, count in result.items():
630
+ print(f" {old_name}/{split}: {count} rows (alias of {new_name})")
631
+ all_results[old_name] = result
632
+
633
+ # Summary
634
+ print(f"\n{'='*60}")
635
+ print(f"Generated parquet files for {len(all_results)} configs")
636
+ total_files = sum(1 for d in PARQUET_DIR.rglob("*.parquet"))
637
+ print(f"Total parquet files: {total_files}")
638
+
639
+ # Print total size
640
+ total_bytes = sum(f.stat().st_size for f in PARQUET_DIR.rglob("*.parquet"))
641
+ print(f"Total size: {total_bytes / 1024 / 1024:.1f} MB")
642
+
643
+ return all_results
644
+
645
+
646
+ if __name__ == "__main__":
647
+ main()
labels/GMDCSA24.csv ADDED
@@ -0,0 +1,459 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ path,label,start,end,subject,cam,dataset
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labels/occu.csv ADDED
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labels/of-syn.csv ADDED
The diff for this file is too large to render. See raw diff
 
labels/up_fall.csv ADDED
The diff for this file is too large to render. See raw diff
 
omnifall_builder.py ADDED
@@ -0,0 +1,1192 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection
2
+
3
+ This dataset builder provides unified access to the OmniFall benchmark, which integrates:
4
+ - OF-Staged (OF-Sta): 8 public staged fall detection datasets (~14h single-view)
5
+ - OF-In-the-Wild (OF-ItW): Curated genuine accident videos from OOPS (~2.7h)
6
+ - OF-Synthetic (OF-Syn): 12,000 synthetic videos generated with Wan 2.2 (~17h)
7
+
8
+ All components share a 16-class activity taxonomy. Staged datasets use classes 0-9,
9
+ while OF-ItW and OF-Syn use the full 0-15 range.
10
+ """
11
+
12
+ import os
13
+ import warnings
14
+ import pandas as pd
15
+ import datasets
16
+ from datasets import (
17
+ BuilderConfig,
18
+ GeneratorBasedBuilder,
19
+ Features,
20
+ Value,
21
+ ClassLabel,
22
+ Sequence,
23
+ SplitGenerator,
24
+ Split,
25
+ Video,
26
+ )
27
+
28
+ _CITATION = """\
29
+ @misc{omnifall,
30
+ title={OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection},
31
+ author={David Schneider and Zdravko Marinov and Rafael Baur and Zeyun Zhong and Rodi D\\\"uger and Rainer Stiefelhagen},
32
+ year={2025},
33
+ eprint={2505.19889},
34
+ archivePrefix={arXiv},
35
+ primaryClass={cs.CV},
36
+ url={https://arxiv.org/abs/2505.19889},
37
+ }
38
+ """
39
+
40
+ _DESCRIPTION = """\
41
+ OmniFall is a comprehensive benchmark that unifies staged, in-the-wild, and synthetic
42
+ fall detection datasets under a common 16-class activity taxonomy.
43
+ """
44
+
45
+ _HOMEPAGE = "https://huggingface.co/datasets/simplexsigil2/omnifall"
46
+ _LICENSE = "cc-by-nc-4.0"
47
+
48
+ # 16 activity classes shared across all components
49
+ _ACTIVITY_LABELS = [
50
+ "walk", # 0
51
+ "fall", # 1
52
+ "fallen", # 2
53
+ "sit_down", # 3
54
+ "sitting", # 4
55
+ "lie_down", # 5
56
+ "lying", # 6
57
+ "stand_up", # 7
58
+ "standing", # 8
59
+ "other", # 9
60
+ "kneel_down", # 10
61
+ "kneeling", # 11
62
+ "squat_down", # 12
63
+ "squatting", # 13
64
+ "crawl", # 14
65
+ "jump", # 15
66
+ ]
67
+
68
+ # Demographic and scene metadata categories (OF-Syn only)
69
+ _AGE_GROUPS = [
70
+ "toddlers_1_4", "children_5_12", "teenagers_13_17",
71
+ "young_adults_18_34", "middle_aged_35_64", "elderly_65_plus",
72
+ ]
73
+ _GENDERS = ["male", "female"]
74
+ _SKIN_TONES = [f"mst{i}" for i in range(1, 11)]
75
+ _ETHNICITIES = ["white", "black", "asian", "hispanic_latino", "aian", "nhpi", "mena"]
76
+ _BMI_BANDS = ["underweight", "normal", "overweight", "obese"]
77
+ _HEIGHT_BANDS = ["short", "avg", "tall"]
78
+ _ENVIRONMENTS = ["indoor", "outdoor"]
79
+ _CAMERA_ELEVATIONS = ["eye", "low", "high", "top"]
80
+ _CAMERA_AZIMUTHS = ["front", "rear", "left", "right"]
81
+ _CAMERA_DISTANCES = ["medium", "far"]
82
+ _CAMERA_SHOTS = ["static_wide", "static_medium_wide"]
83
+ _SPEEDS = ["24fps_rt", "25fps_rt", "30fps_rt", "std_rt"]
84
+
85
+ # The 8 staged datasets
86
+ _STAGED_DATASETS = [
87
+ "caucafall", "cmdfall", "edf", "gmdcsa24",
88
+ "le2i", "mcfd", "occu", "up_fall",
89
+ ]
90
+
91
+ # Label CSV file paths (relative to repo root)
92
+ _STAGED_LABEL_FILES = [f"labels/{name}.csv" for name in [
93
+ "caucafall", "cmdfall", "edf", "GMDCSA24",
94
+ "le2i", "mcfd", "occu", "up_fall",
95
+ ]]
96
+ _ITW_LABEL_FILE = "labels/OOPS.csv"
97
+ _SYN_LABEL_FILE = "labels/of-syn.csv"
98
+ _SYN_VIDEO_ARCHIVE = "data_files/omnifall-synthetic_av1.tar"
99
+
100
+ # OOPS video auto-download configuration
101
+ _OOPS_CACHE_DIR = os.path.join(os.path.expanduser("~"), ".cache", "omnifall", "oops_prepared")
102
+ _OOPS_URL = "https://oops.cs.columbia.edu/data/video_and_anns.tar.gz"
103
+ _OOPS_EXPECTED_VIDEO_COUNT = 818
104
+ _OOPS_MAPPING_FILE = "data_files/oops_video_mapping.csv"
105
+
106
+ _OOPS_LICENSE_TEXT = """\
107
+ ==========================================================================
108
+ OOPS Dataset License Notice
109
+ ==========================================================================
110
+
111
+ The OF-ItW component of OmniFall uses videos from the OOPS dataset.
112
+ The following notice is from the OOPS dataset website
113
+ (https://oops.cs.columbia.edu/data/):
114
+
115
+ "By pressing any of the links above, you acknowledge that we do not
116
+ own the copyright to these videos and that they are solely provided
117
+ for non-commercial research and/or educational purposes. This dataset
118
+ is licensed under a Creative Commons Attribution-NonCommercial-
119
+ ShareAlike 4.0 International License."
120
+
121
+ If you use OF-ItW in your research, please also cite the OOPS paper:
122
+
123
+ @inproceedings{{epstein2020oops,
124
+ title={{Oops! predicting unintentional action in video}},
125
+ author={{Epstein, Dave and Chen, Boyuan and Vondrick, Carl}},
126
+ booktitle={{Proceedings of the IEEE/CVF Conference on Computer
127
+ Vision and Pattern Recognition}},
128
+ pages={{919--929}},
129
+ year={{2020}}
130
+ }}
131
+
132
+ The download will stream ~45GB from the OOPS website and extract {count}
133
+ videos (~2.6GB disk space) to: {cache_dir}
134
+ ==========================================================================
135
+ """
136
+
137
+
138
+ # ---- Feature schema definitions ----
139
+
140
+ def _core_features():
141
+ """7-column schema for staged/OOPS data."""
142
+ return Features({
143
+ "path": Value("string"),
144
+ "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS),
145
+ "start": Value("float32"),
146
+ "end": Value("float32"),
147
+ "subject": Value("int32"),
148
+ "cam": Value("int32"),
149
+ "dataset": Value("string"),
150
+ })
151
+
152
+
153
+ def _syn_features():
154
+ """19-column schema for synthetic data (core + demographic/scene metadata)."""
155
+ return Features({
156
+ "path": Value("string"),
157
+ "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS),
158
+ "start": Value("float32"),
159
+ "end": Value("float32"),
160
+ "subject": Value("int32"),
161
+ "cam": Value("int32"),
162
+ "dataset": Value("string"),
163
+ # Demographic metadata
164
+ "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
165
+ "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
166
+ "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
167
+ "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
168
+ "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
169
+ "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
170
+ # Scene metadata
171
+ "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
172
+ "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
173
+ "speed": ClassLabel(num_classes=4, names=_SPEEDS),
174
+ "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
175
+ "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
176
+ "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
177
+ })
178
+
179
+
180
+ def _syn_metadata_features():
181
+ """Feature schema for OF-Syn metadata config (video-level, no temporal segments)."""
182
+ return Features({
183
+ "path": Value("string"),
184
+ "dataset": Value("string"),
185
+ "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
186
+ "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
187
+ "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
188
+ "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
189
+ "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
190
+ "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
191
+ "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
192
+ "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
193
+ "speed": ClassLabel(num_classes=4, names=_SPEEDS),
194
+ "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
195
+ "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
196
+ "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
197
+ })
198
+
199
+
200
+ def _syn_framewise_features():
201
+ """Feature schema for OF-Syn frame-wise labels (81 labels per video)."""
202
+ return Features({
203
+ "path": Value("string"),
204
+ "dataset": Value("string"),
205
+ "frame_labels": Sequence(
206
+ ClassLabel(num_classes=16, names=_ACTIVITY_LABELS), length=81
207
+ ),
208
+ "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
209
+ "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
210
+ "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
211
+ "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
212
+ "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
213
+ "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
214
+ "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
215
+ "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
216
+ "speed": ClassLabel(num_classes=4, names=_SPEEDS),
217
+ "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
218
+ "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
219
+ "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
220
+ })
221
+
222
+
223
+ def _paths_only_features():
224
+ """Minimal feature schema for paths-only mode."""
225
+ return Features({"path": Value("string")})
226
+
227
+
228
+ # ---- Config ----
229
+
230
+ class OmniFallConfig(BuilderConfig):
231
+ """BuilderConfig for OmniFall dataset.
232
+
233
+ Args:
234
+ config_type: What kind of data to load.
235
+ "labels" - All labels in a single split (no train/val/test).
236
+ "split" - Train/val/test splits from split CSV files.
237
+ "metadata" - Video-level metadata (OF-Syn only).
238
+ "framewise" - Frame-wise HDF5 labels (OF-Syn only).
239
+ data_source: Which component(s) to load.
240
+ "staged" - 8 staged lab datasets
241
+ "itw" - OOPS in-the-wild
242
+ "syn" - OF-Syn synthetic
243
+ "staged+itw" - Staged and OOPS combined
244
+ Individual dataset names (e.g. "cmdfall") for single datasets.
245
+ split_type: Split strategy.
246
+ "cs" / "cv" for staged/OOPS, "random" / "cross_age" / etc. for synthetic.
247
+ train_source: For cross-domain configs, overrides data_source for train/val.
248
+ test_source: For cross-domain configs, overrides data_source for test.
249
+ test_split_type: For cross-domain configs, overrides split_type for test.
250
+ paths_only: If True, only return video paths (no label merging).
251
+ framewise: If True, load frame-wise labels from HDF5 (OF-Syn only).
252
+ include_video: If True, download and include video files.
253
+ For OF-Syn configs, videos are downloaded from the HF repo.
254
+ For OF-ItW configs, requires oops_video_dir to be set.
255
+ decode_video: If True (default), use Video() feature for auto-decoding.
256
+ If False, return absolute file path as string.
257
+ oops_video_dir: Path to directory containing prepared OOPS videos
258
+ (produced by prepare_oops_videos.py). Required when loading
259
+ OF-ItW configs with include_video=True.
260
+ deprecated_alias_for: If set, this config is a deprecated alias.
261
+ """
262
+
263
+ def __init__(
264
+ self,
265
+ config_type="labels",
266
+ data_source="staged+itw",
267
+ split_type=None,
268
+ train_source=None,
269
+ test_source=None,
270
+ test_split_type=None,
271
+ paths_only=False,
272
+ framewise=False,
273
+ include_video=False,
274
+ decode_video=True,
275
+ oops_video_dir=None,
276
+ deprecated_alias_for=None,
277
+ **kwargs,
278
+ ):
279
+ super().__init__(**kwargs)
280
+ self.config_type = config_type
281
+ self.data_source = data_source
282
+ self.split_type = split_type
283
+ self.train_source = train_source
284
+ self.test_source = test_source
285
+ self.test_split_type = test_split_type
286
+ self.paths_only = paths_only
287
+ self.framewise = framewise
288
+ self.include_video = include_video
289
+ self.decode_video = decode_video
290
+ self.oops_video_dir = oops_video_dir
291
+ self.deprecated_alias_for = deprecated_alias_for
292
+
293
+ @property
294
+ def is_crossdomain(self):
295
+ return self.train_source is not None
296
+
297
+
298
+ def _make_config(name, description, **kwargs):
299
+ """Helper to create a config with consistent version."""
300
+ return OmniFallConfig(
301
+ name=name,
302
+ version=datasets.Version("2.0.0"),
303
+ description=description,
304
+ **kwargs,
305
+ )
306
+
307
+
308
+ # ---- Config definitions ----
309
+
310
+ _LABELS_CONFIGS = [
311
+ _make_config(
312
+ "labels",
313
+ "All staged + OOPS labels (52k segments, 7 columns). Default config.",
314
+ config_type="labels",
315
+ data_source="staged+itw",
316
+ ),
317
+ _make_config(
318
+ "labels-syn",
319
+ "OF-Syn labels with demographic metadata (19k segments, 19 columns).",
320
+ config_type="labels",
321
+ data_source="syn",
322
+ ),
323
+ _make_config(
324
+ "metadata-syn",
325
+ "OF-Syn video-level metadata (12k videos, no temporal segments).",
326
+ config_type="metadata",
327
+ data_source="syn",
328
+ ),
329
+ _make_config(
330
+ "framewise-syn",
331
+ "OF-Syn frame-wise labels from HDF5 (81 labels per video).",
332
+ config_type="framewise",
333
+ data_source="syn",
334
+ framewise=True,
335
+ ),
336
+ ]
337
+
338
+ _AGGREGATE_CONFIGS = [
339
+ _make_config(
340
+ "cs",
341
+ "Cross-subject splits for all staged + OOPS datasets combined.",
342
+ config_type="split",
343
+ data_source="staged+itw",
344
+ split_type="cs",
345
+ ),
346
+ _make_config(
347
+ "cv",
348
+ "Cross-view splits for all staged + OOPS datasets combined.",
349
+ config_type="split",
350
+ data_source="staged+itw",
351
+ split_type="cv",
352
+ ),
353
+ ]
354
+
355
+ _PRIMARY_CONFIGS = [
356
+ _make_config(
357
+ "of-sta-cs",
358
+ "OF-Staged: 8 staged datasets, cross-subject splits.",
359
+ config_type="split",
360
+ data_source="staged",
361
+ split_type="cs",
362
+ ),
363
+ _make_config(
364
+ "of-sta-cv",
365
+ "OF-Staged: 8 staged datasets, cross-view splits.",
366
+ config_type="split",
367
+ data_source="staged",
368
+ split_type="cv",
369
+ ),
370
+ _make_config(
371
+ "of-itw",
372
+ "OF-ItW: OOPS-Fall in-the-wild genuine accidents.",
373
+ config_type="split",
374
+ data_source="itw",
375
+ split_type="cs",
376
+ ),
377
+ _make_config(
378
+ "of-syn",
379
+ "OF-Syn: synthetic, random 80/10/10 split.",
380
+ config_type="split",
381
+ data_source="syn",
382
+ split_type="random",
383
+ ),
384
+ _make_config(
385
+ "of-syn-cross-age",
386
+ "OF-Syn: cross-age split (train: adults, test: children/elderly).",
387
+ config_type="split",
388
+ data_source="syn",
389
+ split_type="cross_age",
390
+ ),
391
+ _make_config(
392
+ "of-syn-cross-ethnicity",
393
+ "OF-Syn: cross-ethnicity split.",
394
+ config_type="split",
395
+ data_source="syn",
396
+ split_type="cross_ethnicity",
397
+ ),
398
+ _make_config(
399
+ "of-syn-cross-bmi",
400
+ "OF-Syn: cross-BMI split (train: normal/underweight, test: obese).",
401
+ config_type="split",
402
+ data_source="syn",
403
+ split_type="cross_bmi",
404
+ ),
405
+ ]
406
+
407
+ _CROSSDOMAIN_CONFIGS = [
408
+ _make_config(
409
+ "of-sta-itw-cs",
410
+ "Cross-domain: train/val on staged CS, test on OOPS.",
411
+ config_type="split",
412
+ data_source="staged",
413
+ split_type="cs",
414
+ train_source="staged",
415
+ test_source="itw",
416
+ test_split_type="cs",
417
+ ),
418
+ _make_config(
419
+ "of-sta-itw-cv",
420
+ "Cross-domain: train/val on staged CV, test on OOPS.",
421
+ config_type="split",
422
+ data_source="staged",
423
+ split_type="cv",
424
+ train_source="staged",
425
+ test_source="itw",
426
+ test_split_type="cv",
427
+ ),
428
+ _make_config(
429
+ "of-syn-itw",
430
+ "Cross-domain: train/val on OF-Syn random, test on OOPS.",
431
+ config_type="split",
432
+ data_source="syn",
433
+ split_type="random",
434
+ train_source="syn",
435
+ test_source="itw",
436
+ test_split_type="cs",
437
+ ),
438
+ ]
439
+
440
+ _INDIVIDUAL_CONFIGS = [
441
+ _make_config(
442
+ name,
443
+ f"{name} dataset with cross-subject splits.",
444
+ config_type="split",
445
+ data_source=name,
446
+ split_type="cs",
447
+ )
448
+ for name in _STAGED_DATASETS
449
+ ]
450
+
451
+ # Deprecated aliases: defined with full correct attributes so _info() works
452
+ # immediately (HF calls _info() during __init__, before any custom init code).
453
+ _DEPRECATED_ALIASES = {
454
+ "cs-staged": "of-sta-cs",
455
+ "cv-staged": "of-sta-cv",
456
+ "cs-staged-wild": "of-sta-itw-cs",
457
+ "cv-staged-wild": "of-sta-itw-cv",
458
+ "OOPS": "of-itw",
459
+ }
460
+
461
+ # Build a lookup from config name to config object
462
+ _ALL_NAMED_CONFIGS = {
463
+ cfg.name: cfg
464
+ for cfg in (
465
+ _LABELS_CONFIGS + _AGGREGATE_CONFIGS + _PRIMARY_CONFIGS
466
+ + _CROSSDOMAIN_CONFIGS + _INDIVIDUAL_CONFIGS
467
+ )
468
+ }
469
+
470
+ _DEPRECATED_CONFIGS = []
471
+ for _old_name, _new_name in _DEPRECATED_ALIASES.items():
472
+ _target = _ALL_NAMED_CONFIGS[_new_name]
473
+ _DEPRECATED_CONFIGS.append(
474
+ _make_config(
475
+ _old_name,
476
+ f"DEPRECATED: Use '{_new_name}' instead.",
477
+ config_type=_target.config_type,
478
+ data_source=_target.data_source,
479
+ split_type=_target.split_type,
480
+ train_source=_target.train_source,
481
+ test_source=_target.test_source,
482
+ test_split_type=_target.test_split_type,
483
+ paths_only=_target.paths_only,
484
+ framewise=_target.framewise,
485
+ include_video=_target.include_video,
486
+ decode_video=_target.decode_video,
487
+ oops_video_dir=_target.oops_video_dir,
488
+ deprecated_alias_for=_new_name,
489
+ )
490
+ )
491
+
492
+
493
+ # ---- Builder ----
494
+
495
+ class OmniFall(GeneratorBasedBuilder):
496
+ """OmniFall unified fall detection benchmark builder."""
497
+
498
+ VERSION = datasets.Version("2.0.0")
499
+ BUILDER_CONFIG_CLASS = OmniFallConfig
500
+
501
+ BUILDER_CONFIGS = (
502
+ _LABELS_CONFIGS
503
+ + _AGGREGATE_CONFIGS
504
+ + _PRIMARY_CONFIGS
505
+ + _CROSSDOMAIN_CONFIGS
506
+ + _INDIVIDUAL_CONFIGS
507
+ + _DEPRECATED_CONFIGS
508
+ )
509
+
510
+ DEFAULT_CONFIG_NAME = "labels"
511
+
512
+ def _info(self):
513
+ """Return dataset metadata and feature schema."""
514
+ cfg = self.config
515
+
516
+ if cfg.config_type == "metadata":
517
+ features = _syn_metadata_features()
518
+ elif cfg.framewise:
519
+ features = _syn_framewise_features()
520
+ elif cfg.paths_only:
521
+ features = _paths_only_features()
522
+ elif cfg.is_crossdomain:
523
+ # Cross-domain configs mix sources, use common 7-col schema
524
+ features = _core_features()
525
+ elif cfg.data_source == "syn":
526
+ features = _syn_features()
527
+ else:
528
+ features = _core_features()
529
+
530
+ if cfg.include_video:
531
+ features["video"] = Video() if cfg.decode_video else Value("string")
532
+
533
+ return datasets.DatasetInfo(
534
+ description=_DESCRIPTION,
535
+ features=features,
536
+ homepage=_HOMEPAGE,
537
+ license=_LICENSE,
538
+ citation=_CITATION,
539
+ )
540
+
541
+ # ---- Split generators ----
542
+
543
+ def _split_generators(self, dl_manager):
544
+ cfg = self.config
545
+
546
+ # Emit deprecation warning
547
+ if cfg.deprecated_alias_for:
548
+ warnings.warn(
549
+ f"Config '{cfg.name}' is deprecated. "
550
+ f"Use '{cfg.deprecated_alias_for}' instead.",
551
+ DeprecationWarning,
552
+ stacklevel=2,
553
+ )
554
+
555
+ # Labels configs: all data in a single "train" split
556
+ if cfg.config_type == "labels":
557
+ return self._labels_splits(cfg, dl_manager)
558
+
559
+ # Metadata config
560
+ if cfg.config_type == "metadata":
561
+ metadata_path = dl_manager.download("videos/metadata.csv")
562
+ return [
563
+ SplitGenerator(
564
+ name=Split.TRAIN,
565
+ gen_kwargs={"mode": "metadata", "metadata_path": metadata_path},
566
+ ),
567
+ ]
568
+
569
+ # Framewise config (no split, all data)
570
+ if cfg.config_type == "framewise":
571
+ archive_path = dl_manager.download_and_extract(
572
+ "data_files/syn_frame_wise_labels.tar.zst"
573
+ )
574
+ metadata_path = dl_manager.download("videos/metadata.csv")
575
+ return [
576
+ SplitGenerator(
577
+ name=Split.TRAIN,
578
+ gen_kwargs={
579
+ "mode": "framewise",
580
+ "hdf5_dir": archive_path,
581
+ "metadata_path": metadata_path,
582
+ "split_file": None,
583
+ },
584
+ ),
585
+ ]
586
+
587
+ # Split configs (train/val/test)
588
+ if cfg.config_type == "split":
589
+ return self._split_config_generators(cfg, dl_manager)
590
+
591
+ raise ValueError(f"Unknown config_type: {cfg.config_type}")
592
+
593
+ def _labels_splits(self, cfg, dl_manager):
594
+ """Generate split generators for labels-type configs."""
595
+ if cfg.data_source == "syn":
596
+ filepath = dl_manager.download(_SYN_LABEL_FILE)
597
+ return [
598
+ SplitGenerator(
599
+ name=Split.TRAIN,
600
+ gen_kwargs={"mode": "csv_direct", "filepath": filepath},
601
+ ),
602
+ ]
603
+ elif cfg.data_source == "staged+itw":
604
+ filepaths = dl_manager.download(_STAGED_LABEL_FILES + [_ITW_LABEL_FILE])
605
+ return [
606
+ SplitGenerator(
607
+ name=Split.TRAIN,
608
+ gen_kwargs={"mode": "csv_multi", "filepaths": filepaths},
609
+ ),
610
+ ]
611
+ else:
612
+ raise ValueError(f"Unsupported data_source for labels: {cfg.data_source}")
613
+
614
+ def _split_config_generators(self, cfg, dl_manager):
615
+ """Generate split generators for train/val/test split configs."""
616
+ if cfg.is_crossdomain:
617
+ return self._crossdomain_splits(cfg, dl_manager)
618
+
619
+ if cfg.data_source == "syn":
620
+ return self._syn_splits(cfg, dl_manager)
621
+ elif cfg.data_source == "staged":
622
+ return self._staged_splits(cfg, dl_manager)
623
+ elif cfg.data_source == "itw":
624
+ return self._itw_splits(cfg, dl_manager)
625
+ elif cfg.data_source == "staged+itw":
626
+ return self._aggregate_splits(cfg, dl_manager)
627
+ elif cfg.data_source in _STAGED_DATASETS:
628
+ return self._individual_splits(cfg, dl_manager)
629
+ else:
630
+ raise ValueError(f"Unknown data_source: {cfg.data_source}")
631
+
632
+ def _staged_split_files(self, split_type, split_name):
633
+ """Return list of split CSV paths for all 8 staged datasets."""
634
+ return [f"splits/{split_type}/{ds}/{split_name}.csv" for ds in _STAGED_DATASETS]
635
+
636
+ def _resolve_oops_video_dir(self, cfg, dl_manager):
637
+ """Resolve the OOPS video directory for OF-ItW configs.
638
+
639
+ Priority:
640
+ 1. If include_video is False, return None.
641
+ 2. If oops_video_dir is explicitly provided, validate and return it.
642
+ 3. If cache exists with expected video count, return cache path.
643
+ 4. Otherwise, prompt for license consent and auto-download.
644
+ """
645
+ if not cfg.include_video:
646
+ return None
647
+
648
+ # User explicitly provided a directory
649
+ if cfg.oops_video_dir:
650
+ video_dir = os.path.abspath(cfg.oops_video_dir)
651
+ if not os.path.isdir(video_dir):
652
+ raise FileNotFoundError(
653
+ f"oops_video_dir does not exist: {video_dir}\n"
654
+ "Run prepare_oops_videos.py to prepare OOPS videos first."
655
+ )
656
+ return video_dir
657
+
658
+ # Check cache
659
+ cache_dir = _OOPS_CACHE_DIR
660
+ if self._oops_cache_is_valid(cache_dir):
661
+ return cache_dir
662
+
663
+ # Auto-download: prompt for consent and extract
664
+ return self._auto_prepare_oops(cache_dir, dl_manager)
665
+
666
+ def _oops_cache_is_valid(self, cache_dir):
667
+ """Check if the OOPS video cache contains the expected number of videos."""
668
+ falls_dir = os.path.join(cache_dir, "falls")
669
+ if not os.path.isdir(falls_dir):
670
+ return False
671
+ mp4_count = sum(1 for f in os.listdir(falls_dir) if f.endswith(".mp4"))
672
+ if mp4_count >= _OOPS_EXPECTED_VIDEO_COUNT:
673
+ return True
674
+ if mp4_count > 0:
675
+ warnings.warn(
676
+ f"OOPS cache at {cache_dir} contains {mp4_count}/{_OOPS_EXPECTED_VIDEO_COUNT} "
677
+ f"videos (incomplete). Will re-download."
678
+ )
679
+ return False
680
+
681
+ def _auto_prepare_oops(self, cache_dir, dl_manager):
682
+ """Download and prepare OOPS videos with interactive license consent."""
683
+ import csv
684
+ import subprocess
685
+ import tarfile
686
+
687
+ # Print license and get consent
688
+ print(_OOPS_LICENSE_TEXT.format(
689
+ count=_OOPS_EXPECTED_VIDEO_COUNT, cache_dir=cache_dir,
690
+ ))
691
+ try:
692
+ response = input('Type "YES" to accept the license and begin download: ')
693
+ except EOFError:
694
+ raise RuntimeError(
695
+ "Cannot prompt for OOPS license consent in non-interactive mode.\n"
696
+ "Either run prepare_oops_videos.py manually and pass oops_video_dir,\n"
697
+ "or run this script in an interactive terminal."
698
+ )
699
+
700
+ if response.strip() != "YES":
701
+ raise RuntimeError(
702
+ "OOPS license not accepted. To load OF-ItW with videos, either:\n"
703
+ "1. Run again and type YES when prompted, or\n"
704
+ "2. Run prepare_oops_videos.py manually and pass oops_video_dir."
705
+ )
706
+
707
+ # Download the mapping file from the HF repo
708
+ mapping_path = dl_manager.download(_OOPS_MAPPING_FILE)
709
+ mapping = {}
710
+ with open(mapping_path) as f:
711
+ reader = csv.DictReader(f)
712
+ for row in reader:
713
+ mapping[row["oops_path"]] = row["itw_path"]
714
+
715
+ # Create output directory
716
+ os.makedirs(os.path.join(cache_dir, "falls"), exist_ok=True)
717
+
718
+ # Extract videos
719
+ found = self._extract_oops_videos(_OOPS_URL, mapping, cache_dir)
720
+
721
+ if found == 0:
722
+ raise RuntimeError(
723
+ "Failed to extract any OOPS videos. Check network connectivity "
724
+ "and try again, or use prepare_oops_videos.py with a local archive."
725
+ )
726
+
727
+ if found < _OOPS_EXPECTED_VIDEO_COUNT:
728
+ warnings.warn(
729
+ f"Only extracted {found}/{_OOPS_EXPECTED_VIDEO_COUNT} OOPS videos. "
730
+ f"Some videos may be missing from the archive."
731
+ )
732
+
733
+ return cache_dir
734
+
735
+ def _extract_oops_videos(self, source, mapping, output_dir):
736
+ """Stream through the OOPS archive and extract matching videos."""
737
+ import subprocess
738
+ import tarfile
739
+
740
+ total = len(mapping)
741
+ print(f"Extracting {total} videos from OOPS archive...")
742
+ print("(Streaming ~45GB from web, no local disk space needed for archive)")
743
+ print("(This may take 30-60 minutes depending on connection speed)")
744
+
745
+ os.makedirs(os.path.join(output_dir, "falls"), exist_ok=True)
746
+
747
+ found = 0
748
+ remaining = set(mapping.keys())
749
+
750
+ cmd = f'curl -sL "{source}" | tar -xzf - --to-stdout "oops_dataset/video.tar.gz"'
751
+ proc = subprocess.Popen(
752
+ cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
753
+ )
754
+
755
+ try:
756
+ with tarfile.open(fileobj=proc.stdout, mode="r|gz") as tar:
757
+ for member in tar:
758
+ if not remaining:
759
+ break
760
+ if member.name in remaining:
761
+ itw_path = mapping[member.name]
762
+ out_path = os.path.join(output_dir, itw_path)
763
+
764
+ f = tar.extractfile(member)
765
+ if f is not None:
766
+ with open(out_path, "wb") as out_f:
767
+ while True:
768
+ chunk = f.read(1024 * 1024)
769
+ if not chunk:
770
+ break
771
+ out_f.write(chunk)
772
+ f.close()
773
+ found += 1
774
+ remaining.discard(member.name)
775
+ if found % 50 == 0:
776
+ print(f" Extracted {found}/{total} videos...")
777
+ finally:
778
+ proc.stdout.close()
779
+ proc.wait()
780
+
781
+ print(f"Extracted {found}/{total} videos to {output_dir}")
782
+ if remaining:
783
+ print(f"WARNING: {len(remaining)} videos not found in archive.")
784
+
785
+ return found
786
+
787
+ def _make_split_merge_generators(self, split_files_per_split, label_files,
788
+ dl_manager, video_dir=None):
789
+ """Helper to create train/val/test SplitGenerators for split_merge mode.
790
+
791
+ Args:
792
+ split_files_per_split: dict mapping split name to list of relative paths.
793
+ label_files: list of relative label file paths.
794
+ dl_manager: download manager for resolving paths.
795
+ video_dir: path to extracted video directory, or None.
796
+ """
797
+ resolved_labels = dl_manager.download(label_files)
798
+ return [
799
+ SplitGenerator(
800
+ name=split_enum,
801
+ gen_kwargs={
802
+ "mode": "split_merge",
803
+ "split_files": dl_manager.download(split_files_per_split[csv_name]),
804
+ "label_files": resolved_labels,
805
+ "video_dir": video_dir,
806
+ },
807
+ )
808
+ for split_enum, csv_name in [
809
+ (Split.TRAIN, "train"),
810
+ (Split.VALIDATION, "val"),
811
+ (Split.TEST, "test"),
812
+ ]
813
+ ]
814
+
815
+ def _staged_splits(self, cfg, dl_manager):
816
+ """OF-Staged: 8 datasets combined with CS or CV splits."""
817
+ st = cfg.split_type
818
+ return self._make_split_merge_generators(
819
+ {sn: self._staged_split_files(st, sn) for sn in ("train", "val", "test")},
820
+ _STAGED_LABEL_FILES,
821
+ dl_manager,
822
+ )
823
+
824
+ def _itw_splits(self, cfg, dl_manager):
825
+ """OF-ItW: OOPS-Fall (CS=CV identical)."""
826
+ st = cfg.split_type
827
+ video_dir = self._resolve_oops_video_dir(cfg, dl_manager)
828
+ return self._make_split_merge_generators(
829
+ {sn: [f"splits/{st}/OOPS/{sn}.csv"] for sn in ("train", "val", "test")},
830
+ [_ITW_LABEL_FILE],
831
+ dl_manager,
832
+ video_dir=video_dir,
833
+ )
834
+
835
+ def _aggregate_splits(self, cfg, dl_manager):
836
+ """All staged + OOPS combined (cs or cv)."""
837
+ st = cfg.split_type
838
+ all_labels = _STAGED_LABEL_FILES + [_ITW_LABEL_FILE]
839
+ return self._make_split_merge_generators(
840
+ {sn: self._staged_split_files(st, sn) + [f"splits/{st}/OOPS/{sn}.csv"]
841
+ for sn in ("train", "val", "test")},
842
+ all_labels,
843
+ dl_manager,
844
+ )
845
+
846
+ def _individual_splits(self, cfg, dl_manager):
847
+ """Individual dataset with CS splits."""
848
+ ds_name = cfg.data_source
849
+ label_file_map = {
850
+ "caucafall": "labels/caucafall.csv",
851
+ "cmdfall": "labels/cmdfall.csv",
852
+ "edf": "labels/edf.csv",
853
+ "gmdcsa24": "labels/GMDCSA24.csv",
854
+ "le2i": "labels/le2i.csv",
855
+ "mcfd": "labels/mcfd.csv",
856
+ "occu": "labels/occu.csv",
857
+ "up_fall": "labels/up_fall.csv",
858
+ }
859
+ label_file = label_file_map[ds_name]
860
+ st = cfg.split_type
861
+ return self._make_split_merge_generators(
862
+ {sn: [f"splits/{st}/{ds_name}/{sn}.csv"] for sn in ("train", "val", "test")},
863
+ [label_file],
864
+ dl_manager,
865
+ )
866
+
867
+ def _syn_splits(self, cfg, dl_manager):
868
+ """OF-Syn split strategies."""
869
+ st = cfg.split_type
870
+ split_dir = f"splits/syn/{st}"
871
+
872
+ # Download video archive if requested
873
+ video_dir = None
874
+ if cfg.include_video:
875
+ video_dir = dl_manager.download_and_extract(_SYN_VIDEO_ARCHIVE)
876
+
877
+ if cfg.framewise:
878
+ archive_path = dl_manager.download_and_extract(
879
+ "data_files/syn_frame_wise_labels.tar.zst"
880
+ )
881
+ metadata_path = dl_manager.download("videos/metadata.csv")
882
+ split_files = dl_manager.download(
883
+ {sn: f"{split_dir}/{sn}.csv" for sn in ("train", "val", "test")}
884
+ )
885
+ return [
886
+ SplitGenerator(
887
+ name=split_enum,
888
+ gen_kwargs={
889
+ "mode": "framewise",
890
+ "hdf5_dir": archive_path,
891
+ "metadata_path": metadata_path,
892
+ "split_file": split_files[csv_name],
893
+ },
894
+ )
895
+ for split_enum, csv_name in [
896
+ (Split.TRAIN, "train"),
897
+ (Split.VALIDATION, "val"),
898
+ (Split.TEST, "test"),
899
+ ]
900
+ ]
901
+
902
+ if cfg.paths_only:
903
+ split_files = dl_manager.download(
904
+ {sn: f"{split_dir}/{sn}.csv" for sn in ("train", "val", "test")}
905
+ )
906
+ return [
907
+ SplitGenerator(
908
+ name=split_enum,
909
+ gen_kwargs={
910
+ "mode": "paths_only",
911
+ "split_file": split_files[csv_name],
912
+ },
913
+ )
914
+ for split_enum, csv_name in [
915
+ (Split.TRAIN, "train"),
916
+ (Split.VALIDATION, "val"),
917
+ (Split.TEST, "test"),
918
+ ]
919
+ ]
920
+
921
+ return self._make_split_merge_generators(
922
+ {sn: [f"{split_dir}/{sn}.csv"] for sn in ("train", "val", "test")},
923
+ [_SYN_LABEL_FILE],
924
+ dl_manager,
925
+ video_dir=video_dir,
926
+ )
927
+
928
+ def _crossdomain_splits(self, cfg, dl_manager):
929
+ """Cross-domain configs: train/val from one source, test from another."""
930
+ train_st = cfg.split_type
931
+ test_st = cfg.test_split_type or "cs"
932
+
933
+ # Resolve video directories for each source
934
+ train_video_dir = None
935
+ if cfg.include_video and cfg.train_source == "syn":
936
+ train_video_dir = dl_manager.download_and_extract(_SYN_VIDEO_ARCHIVE)
937
+
938
+ test_video_dir = None
939
+ if cfg.include_video and cfg.test_source == "itw":
940
+ test_video_dir = self._resolve_oops_video_dir(cfg, dl_manager)
941
+
942
+ # Determine train/val files and labels
943
+ if cfg.train_source == "staged":
944
+ train_split_files = {
945
+ sn: self._staged_split_files(train_st, sn)
946
+ for sn in ("train", "val")
947
+ }
948
+ train_labels = _STAGED_LABEL_FILES
949
+ elif cfg.train_source == "syn":
950
+ train_split_files = {
951
+ sn: [f"splits/syn/{train_st}/{sn}.csv"]
952
+ for sn in ("train", "val")
953
+ }
954
+ train_labels = [_SYN_LABEL_FILE]
955
+ else:
956
+ raise ValueError(f"Unsupported train_source: {cfg.train_source}")
957
+
958
+ # Determine test files and labels
959
+ if cfg.test_source == "itw":
960
+ test_split_files = [f"splits/{test_st}/OOPS/test.csv"]
961
+ test_labels = [_ITW_LABEL_FILE]
962
+ else:
963
+ raise ValueError(f"Unsupported test_source: {cfg.test_source}")
964
+
965
+ # Download all paths
966
+ resolved_train_labels = dl_manager.download(train_labels)
967
+ resolved_test_labels = dl_manager.download(test_labels)
968
+ resolved_test_splits = dl_manager.download(test_split_files)
969
+
970
+ return [
971
+ SplitGenerator(
972
+ name=Split.TRAIN,
973
+ gen_kwargs={
974
+ "mode": "split_merge",
975
+ "split_files": dl_manager.download(train_split_files["train"]),
976
+ "label_files": resolved_train_labels,
977
+ "video_dir": train_video_dir,
978
+ },
979
+ ),
980
+ SplitGenerator(
981
+ name=Split.VALIDATION,
982
+ gen_kwargs={
983
+ "mode": "split_merge",
984
+ "split_files": dl_manager.download(train_split_files["val"]),
985
+ "label_files": resolved_train_labels,
986
+ "video_dir": train_video_dir,
987
+ },
988
+ ),
989
+ SplitGenerator(
990
+ name=Split.TEST,
991
+ gen_kwargs={
992
+ "mode": "split_merge",
993
+ "split_files": resolved_test_splits,
994
+ "label_files": resolved_test_labels,
995
+ "video_dir": test_video_dir,
996
+ },
997
+ ),
998
+ ]
999
+
1000
+ # ---- Example generators ----
1001
+
1002
+ def _generate_examples(self, mode, **kwargs):
1003
+ """Dispatch to the appropriate generator based on mode."""
1004
+ if mode == "csv_direct":
1005
+ yield from self._gen_csv_direct(**kwargs)
1006
+ elif mode == "csv_multi":
1007
+ yield from self._gen_csv_multi(**kwargs)
1008
+ elif mode == "split_merge":
1009
+ yield from self._gen_split_merge(**kwargs)
1010
+ elif mode == "metadata":
1011
+ yield from self._gen_metadata(**kwargs)
1012
+ elif mode == "framewise":
1013
+ yield from self._gen_framewise(**kwargs)
1014
+ elif mode == "paths_only":
1015
+ yield from self._gen_paths_only(**kwargs)
1016
+ else:
1017
+ raise ValueError(f"Unknown generation mode: {mode}")
1018
+
1019
+ def _gen_csv_direct(self, filepath):
1020
+ """Load a single CSV file directly."""
1021
+ df = pd.read_csv(filepath)
1022
+ for idx, row in df.iterrows():
1023
+ yield idx, self._row_to_example(row)
1024
+
1025
+ def _gen_csv_multi(self, filepaths):
1026
+ """Load and concatenate multiple CSV files."""
1027
+ dfs = [pd.read_csv(fp) for fp in filepaths]
1028
+ df = pd.concat(dfs, ignore_index=True)
1029
+ for idx, row in df.iterrows():
1030
+ yield idx, self._row_to_example(row)
1031
+
1032
+ def _gen_split_merge(self, split_files, label_files, video_dir=None):
1033
+ """Load split paths, merge with labels, yield examples."""
1034
+ split_dfs = [pd.read_csv(sf) for sf in split_files]
1035
+ split_df = pd.concat(split_dfs, ignore_index=True)
1036
+
1037
+ if self.config.paths_only:
1038
+ for idx, row in split_df.iterrows():
1039
+ yield idx, {"path": row["path"]}
1040
+ return
1041
+
1042
+ label_dfs = [pd.read_csv(lf) for lf in label_files]
1043
+ labels_df = pd.concat(label_dfs, ignore_index=True)
1044
+
1045
+ merged_df = pd.merge(split_df, labels_df, on="path", how="left")
1046
+
1047
+ for idx, row in merged_df.iterrows():
1048
+ example = self._row_to_example(row)
1049
+ if video_dir is not None:
1050
+ example["video"] = os.path.join(video_dir, row["path"] + ".mp4")
1051
+ yield idx, example
1052
+
1053
+ def _gen_metadata(self, metadata_path):
1054
+ """Load OF-Syn video-level metadata."""
1055
+ df = pd.read_csv(metadata_path)
1056
+ metadata_cols = [
1057
+ "path", "age_group", "gender_presentation", "monk_skin_tone",
1058
+ "race_ethnicity_omb", "bmi_band", "height_band",
1059
+ "environment_category", "camera_shot", "speed",
1060
+ "camera_elevation", "camera_azimuth", "camera_distance",
1061
+ ]
1062
+ available_cols = [c for c in metadata_cols if c in df.columns]
1063
+ df = df[available_cols].drop_duplicates(subset=["path"]).reset_index(drop=True)
1064
+ df["dataset"] = "of-syn"
1065
+
1066
+ for idx, row in df.iterrows():
1067
+ yield idx, self._row_to_example(row)
1068
+
1069
+ def _gen_framewise(self, hdf5_dir, metadata_path, split_file=None):
1070
+ """Load frame-wise labels from HDF5 files with metadata."""
1071
+ import h5py
1072
+ import tarfile
1073
+ from pathlib import Path
1074
+
1075
+ metadata_df = pd.read_csv(metadata_path)
1076
+
1077
+ valid_paths = None
1078
+ if split_file is not None:
1079
+ split_df = pd.read_csv(split_file)
1080
+ valid_paths = set(split_df["path"].tolist())
1081
+
1082
+ hdf5_path = Path(hdf5_dir)
1083
+ metadata_fields = [
1084
+ "age_group", "gender_presentation", "monk_skin_tone",
1085
+ "race_ethnicity_omb", "bmi_band", "height_band",
1086
+ "environment_category", "camera_shot", "speed",
1087
+ "camera_elevation", "camera_azimuth", "camera_distance",
1088
+ ]
1089
+
1090
+ if hdf5_path.is_file() and (
1091
+ hdf5_path.suffix == ".tar" or tarfile.is_tarfile(str(hdf5_path))
1092
+ ):
1093
+ idx = 0
1094
+ with tarfile.open(hdf5_path, "r") as tar:
1095
+ for member in tar.getmembers():
1096
+ if not member.name.endswith(".h5"):
1097
+ continue
1098
+ video_path = member.name.lstrip("./").replace(".h5", "")
1099
+ if valid_paths is not None and video_path not in valid_paths:
1100
+ continue
1101
+ try:
1102
+ h5_file = tar.extractfile(member)
1103
+ if h5_file is None:
1104
+ continue
1105
+ import tempfile
1106
+ with tempfile.NamedTemporaryFile(suffix=".h5", delete=True) as tmp:
1107
+ tmp.write(h5_file.read())
1108
+ tmp.flush()
1109
+ with h5py.File(tmp.name, "r") as f:
1110
+ frame_labels = f["label_indices"][:].tolist()
1111
+ video_metadata = metadata_df[metadata_df["path"] == video_path]
1112
+ if len(video_metadata) == 0:
1113
+ continue
1114
+ video_meta = video_metadata.iloc[0]
1115
+ example = {
1116
+ "path": video_path,
1117
+ "dataset": "of-syn",
1118
+ "frame_labels": frame_labels,
1119
+ }
1120
+ for field in metadata_fields:
1121
+ if field in video_meta and pd.notna(video_meta[field]):
1122
+ example[field] = str(video_meta[field])
1123
+ yield idx, example
1124
+ idx += 1
1125
+ except Exception as e:
1126
+ warnings.warn(f"Failed to process {member.name}: {e}")
1127
+ continue
1128
+ else:
1129
+ hdf5_files = sorted(hdf5_path.glob("**/*.h5"))
1130
+ idx = 0
1131
+ for h5_file_path in hdf5_files:
1132
+ relative_path = h5_file_path.relative_to(hdf5_path)
1133
+ video_path = str(relative_path.with_suffix(""))
1134
+ if valid_paths is not None and video_path not in valid_paths:
1135
+ continue
1136
+ try:
1137
+ with h5py.File(h5_file_path, "r") as f:
1138
+ frame_labels = f["label_indices"][:].tolist()
1139
+ video_metadata = metadata_df[metadata_df["path"] == video_path]
1140
+ if len(video_metadata) == 0:
1141
+ continue
1142
+ video_meta = video_metadata.iloc[0]
1143
+ example = {
1144
+ "path": video_path,
1145
+ "dataset": "of-syn",
1146
+ "frame_labels": frame_labels,
1147
+ }
1148
+ for field in metadata_fields:
1149
+ if field in video_meta and pd.notna(video_meta[field]):
1150
+ example[field] = str(video_meta[field])
1151
+ yield idx, example
1152
+ idx += 1
1153
+ except Exception as e:
1154
+ warnings.warn(f"Failed to process {h5_file_path}: {e}")
1155
+ continue
1156
+
1157
+ def _gen_paths_only(self, split_file):
1158
+ """Load paths only from a split file."""
1159
+ df = pd.read_csv(split_file)
1160
+ for idx, row in df.iterrows():
1161
+ yield idx, {"path": row["path"]}
1162
+
1163
+ def _row_to_example(self, row):
1164
+ """Convert a DataFrame row to a typed example dict.
1165
+
1166
+ Only includes fields present in the row. HuggingFace's Features.encode_example()
1167
+ will ignore extra fields and fill missing optional fields.
1168
+ """
1169
+ example = {"path": str(row["path"])}
1170
+
1171
+ # Core temporal fields
1172
+ for field, dtype in [
1173
+ ("label", int), ("start", float), ("end", float),
1174
+ ("subject", int), ("cam", int),
1175
+ ]:
1176
+ if field in row.index and pd.notna(row[field]):
1177
+ example[field] = dtype(row[field])
1178
+
1179
+ if "dataset" in row.index and pd.notna(row["dataset"]):
1180
+ example["dataset"] = str(row["dataset"])
1181
+
1182
+ # Demographic and scene metadata (present only for syn data)
1183
+ for field in [
1184
+ "age_group", "gender_presentation", "monk_skin_tone",
1185
+ "race_ethnicity_omb", "bmi_band", "height_band",
1186
+ "environment_category", "camera_shot", "speed",
1187
+ "camera_elevation", "camera_azimuth", "camera_distance",
1188
+ ]:
1189
+ if field in row.index and pd.notna(row[field]):
1190
+ example[field] = str(row[field])
1191
+
1192
+ return example
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@@ -0,0 +1,3 @@
 
 
 
 
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