data-preparation

# Data preparation Use this sub-skill when a user needs raw videos converted into the directory and JSON formats consumed by 3D-ResNets-PyTorch. For downstream training, validation, or inference flags after data is prepared, route to [training-and-inference](../training-and-inference/SKILL.md); for top-level routing, return to the [root skill](../../SKILL.md). ## Fast routing | User needs | Open/use | | --- | --- | | Extract RGB JPEG frames from Kinetics, ActivityNet, UCF101, HMDB51, or Moments in Time videos | [`scripts/extract_video_frames.py`](scripts/extract_video_frames.py), then [workflows](references/workflows.md#extract-rgb-jpeg-frames) | | Convert raw RGB videos to HDF5 files | [`scripts/extract_video_hdf5.py`](scripts/extract_video_hdf5.py), then [workflows](references/workflows.md#extract-rgb-hdf5-files) | | Build Kinetics/UCF101/HMDB51/MIT annotation JSON files | [`scripts/build_annotation_json.py`](scripts/build_annotation_json.py), then [data formats](references/data-formats.md#annotation-json-schema) | | Add missing ActivityNet `fps` fields | `build_annotation_json.py activitynet-add-fps`, then [ActivityNet workflow](references/workflows.md#activitynet-existing-json-plus-fps) | | Diagnose missing videos, empty loaders, `flow`/`jpg` assertions, or long HDF5 names | [troubleshooting](references/troubleshooting.md) | ## Core rules to preserve - Prepared JPEG layout is normally `<video_root>/<label>/<video_id>/image_00001.jpg`; ActivityNet raw extraction is flat and produces `<video_root>/v_<id>/image_00001.jpg`. - Prepared RGB HDF5 layout is normally `<video_root>/<label>/<video_id>.hdf5` with a variable-length byte dataset named `video`. The bundled HDF5 extractor does **not** create optical-flow datasets. - `--file_type jpg` must be paired with `--input_type rgb`. The repository asserts that `flow` is supported only when `--file_type hdf5`, and flow HDF5 must contain `video_u` and `video_v` datasets prepared elsewhere. - For frame-directory annotations, segments are one-based half-open ranges such as `[1, n_frames + 1]`. For RGB HDF5 annotations, use zero-based half-open ranges `[0, n_frames]`. - ActivityNet JSON must have per-video `fps` values before the ActivityNet loader can convert time segments to frame indices. ## Bundled scripts Run scripts from this sub-skill directory or call them by path: ```bash python scripts/extract_video_frames.py -h python scripts/extract_video_hdf5.py -h python scripts/build_annotation_json.py -h python scripts/build_annotation_json.py kinetics -h python scripts/build_annotation_json.py activitynet-add-fps -h ``` The scripts are self-contained adaptations of the repository utilities; they do not import from or link to the source checkout.