# React data curation pipeline


> **Note (2026-05-18 update):** The one-shot scripts referenced below have been moved out of /tmp/ and into a versioned home at MultimodalData/twm/scripts/. All references in this document have been updated. The previous /tmp/ copies are stale.

The procedure below is what runs between a fresh recording session and a HF
push. Follow it in order; each stage produces a checkable artifact, so a bad
stage doesn't silently propagate downstream.

Source files referenced. The first five live in the recording-machine
`MultimodalData/twm/` checkout. The rest are the curation scripts produced
for this dataset; they currently live in `MultimodalData/twm/scripts/` (development scratchpad)
and should be moved into `MultimodalData/twm/twm/scripts/` so they're
versioned alongside the rest of the codebase.

| Script | Current location | Notes |
|---|---|---|
| `viz.py` | `MultimodalData/twm/` | **Single source of truth for visualization.** Owns the 1280×480 panel builder (left → middle → right cam ordering), projection overlay, OptiTrack helpers, calibration loading, adaptive-shrink GIF encoder. Every other visualization script in the repo imports from here. |
| `data_collection.py` | `MultimodalData/twm/` | Recording loop. `make_preview` is now a re-export of `twm.viz.build_preview_panel`. |
| `compress_h5.py` | `MultimodalData/twm/` | BLOSC → GZIP+shuffle. |
| `visualize.py` | `MultimodalData/twm/` | Interactive replay viewer / MP4 export. Projection overlay on by default, `--no_projection` to disable. Delegates panel + overlay to `twm.viz`. |
| `twm.contact_index` | `MultimodalData/twm/twm/` | Tactile contact metrics → `.pt` files. |
| `make_episode_gifs_v3.py` | `MultimodalData/twm/scripts/` | Per-episode HF preview GIFs (canonical viewer layout). |
| `freeze_diagnose.py` | `MultimodalData/twm/scripts/` | Augmented freeze detector: cross-modal motion + frame-duplicate + OT-gap + rotation/translation teleport. Classifies each freeze into `pipeline_stall` / `ot_dropout` / `ot_teleport` / `real_still`. |
| `inspect_freezes_v3.py` | `MultimodalData/twm/scripts/` | Helper layer used by `freeze_diagnose.py` — schedules clips per freeze event, renders them via `twm.viz`. |
| `freeze_cut_report.py` | `MultimodalData/twm/scripts/` | Per-threshold tally of frozen-pose frames (no clip rendering). |

The pipeline assumes the standard layout:

```
/media/yxma/Disk1/twm/
├── data/<task>/<date>/episode_*.h5             # raw H5 (BLOSC)
├── processed/<mode>/<task>/<date>/episode_*.pt # contact-indexed .pt
└── figures/
    ├── dataset_figures/                        # summary plots + JSONs
    └── episode_previews/<task>/<date>/         # one GIF per episode
```

`<date>` is `YYYY-MM-DD`; `<task>` is e.g. `motherboard`.

---

## Stage 0 — Recording

Use `data_collection.py`. Confirm before you stop the run:
- All three RealSense cams are streaming (preview window shows three rows).
- Both GelSight feeds are alive.
- All three OptiTrack rigid bodies (left, right, work-piece) are tracked
  for the whole take. If a body's pose stops updating mid-run, the
  recorder will silently hold the last sample → this is the failure mode
  detected in Stage 5C. Re-aim the cameras or re-seat the markers before
  starting a long take.
- Audible buffer-drop warnings are zero in the last 30 s of the session.

Output: `data/<task>/<date>/episode_*.h5` (BLOSC-compressed).

---

## Stage 1 — Integrity check

For every new H5, confirm it opens and that frame counts agree across
modalities. The fast version:

```bash
python - <<'PY'
import h5py, hdf5plugin, sys
from pathlib import Path
ROOT = Path("/media/yxma/Disk1/twm/data/motherboard/<date>")
for p in sorted(ROOT.glob("episode_*.h5")):
    try:
        f = h5py.File(p, "r")
        T = f["realsense/cam0/color"].shape[0]
        for k in ("realsense/cam1/color", "realsense/cam2/color",
                 "gelsight/left/frames", "gelsight/right/frames"):
            assert f[k].shape[0] == T, (p, k, f[k].shape[0], T)
        f.close()
        print(f"OK   {p.name}  T={T}")
    except Exception as e:
        print(f"BAD  {p.name}  {e}")
        p.rename(p.with_suffix(p.suffix + ".corrupt"))
PY
```

Anything that fails to open is renamed `*.h5.corrupt` and excluded from the
rest of the pipeline.

---

## Stage 2 — Compression

BLOSC LZ4 is fast for recording but needs `hdf5plugin` at read time. Convert
to GZIP+shuffle for portability and ~30–50 % size savings:

```bash
python -m twm.compress_h5 \
    --in_dir  /media/yxma/Disk1/twm/data/motherboard/<date> \
    --in_place
```

The script clamps chunk shapes when datasets are shorter than the default
chunk (relevant for short test recordings). Re-run integrity check after.

---

## Stage 3 — Visual QA preview MP4 (per episode)

```bash
# Projection overlay is on by default; the default --cam_calib list already
# includes all three cameras.
python -m twm.visualize \
    /media/yxma/Disk1/twm/data/motherboard/<date> \
    --save_videos

# Same but no overlay (faster, useful for raw-stream sanity checks):
python -m twm.visualize \
    /media/yxma/Disk1/twm/data/motherboard/<date> \
    --save_videos --no_projection
```

These MP4s are for the operator only — scrub each one and flag:
- partial occlusions or out-of-frame moments,
- visible OT dropouts (the projection dot stays still while the hand moves),
- color/exposure problems,
- per-date "active sensors" (i.e. did you only use one side?).

Write findings into `tasks.json:per_date_notes.<date>`.

---

## Stage 4 — Contact indexing → `.pt`

Re-run for the new date:

```bash
python -m twm.contact_index \
    --in_root  /media/yxma/Disk1/twm/data/motherboard/<date> \
    --out_root /media/yxma/Disk1/twm/processed/mode1_v1/motherboard/<date>
```

This downsamples cam0 to 128×128, builds the three contact metrics per side
(intensity / area / mixed), and stores reference-frame metadata in
`_contact_meta`. Reference strategy is `p01` (the 1st-percentile of
intensity over the whole episode), which is robust against early-frame
calibration drift.

Sanity check:

```bash
python - <<'PY'
import torch
p = "/media/yxma/Disk1/twm/processed/mode1_v1/motherboard/<date>/episode_006.pt"
ep = torch.load(p, weights_only=False)
print(list(ep.keys()))
print("T:", ep["view"].shape[0])
print("ref drift L/R:", ep["_contact_meta"]["drift_left"], ep["_contact_meta"]["drift_right"])
PY
```

If either drift > 5.0 the gel surface moved significantly between the
reference frame and the rest of the episode — flag the episode.

---

## Stage 5 — Automated failure-mode detection

Three failure modes are detected from the new `.pt` files and logged to
`figures/dataset_figures/bad_frames.json` + summarised in
`data_quality_breakdown.json`.

### 5A. Tactile intensity spikes (LED flicker)

A frame whose tactile intensity exceeds `p999 + 30 (uint8)` is treated as
a momentary LED dropout. Stored as `intensity_spikes: [[a, b], ...]` per
episode, with a 3-frame buffer on each side.

### 5B. Pose teleports

Per-frame OT velocity > 5 m/s → impossible motion → marker swap or brief
solver glitch. Stored as `pose_teleports_L` / `pose_teleports_R`.

### 5C. Pose freezes — disambiguated by cross-modal motion

A sensor whose 7-vec pose is bitwise-identical across consecutive frames
for ≥ 0.25 s **on an active sensor** is a freeze candidate. Each candidate
is then diagnosed by combining:
- max translation velocity in the OT stream within ±1 s
- max angular velocity in the same window (catches solver flips that the
  old 5 m/s translation-only check missed)
- max OT timestamp gap during the interval
- per-stream consecutive-frame duplicate fraction (cam0/1/2 + affected GS)

Each event is then labelled:

- `ot_loss` — the OptiTrack solver lost the rigid body. Signaled by any
  of: OT timestamp gap ≥ 100 ms, translation velocity ≥ 5 m/s near the
  event, or angular velocity ≥ 15 rad/s near the event (solver flip on
  re-acquire). A bitwise-frozen pose with no contradicting evidence also
  defaults to this class. **GelSight is unaffected** — the gel pad just
  isn't deforming during the freeze (sensor moved in air or set down),
  which mimics a frozen stream but isn't one.
- `pipeline_stall` — ALL three RealSense streams duplicate ≥ 30 % of
  frames in the window. Indicates the recorder itself stalled. Not
  observed in the current React dataset.
- `real_still` — no OT-loss signals and cross-modal motion at noise
  floor. Operator deliberately paused.
- `ambiguous` — none of the above.

Empirically across the bimanual set, **every freeze event in React
classifies as `ot_loss`** — the OptiTrack rig is the bottleneck, not the
camera/GelSight capture stack. The right cut is "drop any window
overlapping an ot_loss interval"; this is materially different from a
benign "operator at rest" interpretation. Fixing it requires improving
mocap coverage (camera placement, marker count on each rigid body), not
changes to the recorder.

Current scripts:
- intensity spikes + pose teleports: the original detector that produced
  the current `bad_frames.json` (translation-velocity only; **rotation
  teleports are now caught in 5C via `freeze_diagnose.py`'s angular
  velocity check** but should be folded into the main `bad_frames.json`
  pipeline.)
- pose freezes: `MultimodalData/twm/scripts/freeze_diagnose.py` (cross-modal classifier;
  outputs per-event verdicts + per-class sample clips). Also see
  `MultimodalData/twm/scripts/freeze_cut_report.py` for per-threshold totals across the task.

**To do — consolidation.** These detectors should be merged into a single
`scripts/detect_failures.py` that:
1. iterates `.pt` files under a `--pt_root`,
2. applies all three detectors (with rotation-aware teleport check),
3. writes the union to `bad_frames.json` and a richer per-mode breakdown
   to `freeze_intervals.json` and `data_quality_breakdown.json`,
4. emits the per-episode `data_quality_report.csv`.

Until that exists, the procedure is: re-run each current script in turn
and merge their outputs by hand into `bad_frames.json`.

---

## Stage 6 — Visual QA gallery GIFs (for HF)

```bash
python /home/yxma/MultimodalData/twm/scripts/make_episode_gifs_v3.py \
    --data_root /media/yxma/Disk1/twm/data/motherboard \
    --pt_root   /media/yxma/Disk1/twm/processed/mode1_v1/motherboard \
    --out_root  /media/yxma/Disk1/twm/figures/episode_previews/motherboard \
    --tasks_json yxma_React/tasks.json
```

Settings (all configurable but the defaults are vetted):
- 60 frames evenly across [2 %, 98 %] of the episode
- 3 cameras shown left|middle|right with sensor projection overlay
- 256-color palette (preserves tactile gradients)
- ≤ 4 MB hard cap; on overflow the script first shrinks the spatial
  resolution, then drops palette down to 128 colours, never below
- Output: one GIF per episode under
  `figures/episode_previews/<task>/<date>/episode_*.gif`

These GIFs are uploaded to HF and rendered inline on the dataset card.

---

## Stage 7 — Per-event QA clips for borderline failures

Run only when Stage 5 flags something subtle (short freezes, weak intensity
spikes, single-frame teleports):

```bash
python /home/yxma/MultimodalData/twm/scripts/inspect_short_freezes.py \
    --thr_min_s 1 --thr_max_s 5 \
    --out_dir /media/yxma/Disk1/twm/figures/dataset_figures/freeze_check_short
```

Each event becomes one short GIF (~60 frames) with the affected sensor
circled red. The operator scans these and decides per-event whether to
keep, cut, or merge intervals. Final calls live in `freeze_intervals.json`
under a `manual_verdict: {"keep"|"cut"|"merge"}` field.

---

## Stage 8 — Update metadata

Touch three files before pushing to HF:

1. `tasks.json`
   - Add the new date to `tasks.<task>.dates`.
   - Add a `per_date_notes.<date>` block:
     ```json
     {
       "kind": "session",
       "active_sensors": ["left", "right"],
       "note": "human-readable summary, e.g. 'bimanual; one OT freeze in ep_017'"
     }
     ```
   - Update `n_episode_files`.

2. `bad_frames.json` — automatically refreshed by Stage 5.

3. `data_quality_breakdown.json` — automatically refreshed by Stage 5.

---

## Stage 9 — Docs refresh

If the new session adds material the dataset card or supporting docs
don't already describe, update:
- `README.md` "At a glance" counters (episodes, frames, duration).
- `docs/quality.md` if a new failure mode appeared, or percentages
  changed by > 0.5 %.
- `docs/recording.md` if hardware / capture rate / sensor lineup changed.
- `docs/caveats.md` for any operator-noted anomaly.

---

## Stage 10 — Push to HF

Pre-flight check — these must all be true:

- [ ] every `episode_*.h5` has a matching `episode_*.pt`
- [ ] every `episode_*.pt` has been touched by `twm.contact_index` after
      the most recent recording (timestamps, file size)
- [ ] `tasks.json:per_date_notes.<date>` exists
- [ ] `bad_frames.json:episodes.<date>/<ep>` exists for every episode
- [ ] every episode has a preview GIF under
      `figures/episode_previews/<task>/<date>/`
- [ ] `data_quality_breakdown.json` reflects the new totals

Then upload:

```bash
python - <<'PY'
from huggingface_hub import upload_folder
upload_folder(
    repo_id="yxma/React",
    repo_type="dataset",
    folder_path=".",
    path_in_repo=".",
    commit_message="Add <date> session + curation pipeline outputs",
)
PY
```

(Set `HF_HUB_ENABLE_HF_TRANSFER=1` for fast LFS uploads.)

---

## Channel-order gotcha (read before adding any new visualiser)

The three video streams use **different byte orders** inside the H5 files. Get
this wrong and your previews come out with R/B swapped:

| Stream | H5 byte order | Why |
|---|---|---|
| `realsense/cam{0,1,2}/color` | **BGR** | `pyrealsense2` enabled with `rs.format.bgr8` in `camera_stream/realsense_stream.py` |
| `gelsight/{left,right}/frames` | **RGB** | Converted in `camera_stream/usb_video_stream.py` (`cv2.cvtColor(BGR2RGB)`) before writing |
| `view` in `processed/.../*.pt` | **BGR** | Carved out of cam0 by `twm.contact_index` with no further conversion |
| `tactile_{left,right}` in `.pt` | **RGB** | Carved out of gelsight frames, same as above |

Rules of thumb:

- `cv2.imshow` and `cv2.VideoWriter` expect BGR — RealSense streams + the
  recording UI are end-to-end consistent (and incidentally hide the fact
  that the GelSight thumbnails in `twm.visualize` are technically rendered
  with their R/B swapped).
- `PIL.Image.fromarray` and the GIF encoder interpret bytes as RGB. Anything
  PIL-bound (gallery previews, dataloader sample GIFs) must convert
  RealSense BGR → RGB once. The cheapest way is `arr[..., ::-1]`.
- When reusing `twm.data_collection.make_preview` as the panel builder for a
  GIF, feed the H5 streams in **as-is** so the panel matches the live viewer
  pixel-for-pixel, then `cv2.cvtColor(panel, cv2.COLOR_BGR2RGB)` once at the
  end before saving with PIL.

If you're writing a new visualiser:

```python
import cv2, h5py, hdf5plugin  # noqa: F401
with h5py.File(path, "r") as f:
    cam_rgb = f["realsense/cam0/color"][i][..., ::-1].copy()  # BGR → RGB
    gs_rgb  = f["gelsight/left/frames"][i]                    # already RGB
```

## Failure-mode reference table

| Mode                | Detector                                       | Threshold        | Where logged                              |
|---------------------|------------------------------------------------|------------------|-------------------------------------------|
| LED intensity spike | `tactile_<side>_intensity[t] - p999 > 30`      | uint8 scale      | `bad_frames.json:intensity_spikes`        |
| Pose teleport       | inter-frame velocity                           | > 5 m/s          | `bad_frames.json:pose_teleports_{L,R}`    |
| OT freeze           | pose bitwise-identical for ≥ N frames          | ≥ 1 s (≈30 fr.)  | `bad_frames.json` + `freeze_intervals.json` |
| Contact-ref drift   | `_contact_meta.drift_{side}`                   | > 5.0            | `_contact_meta` (per-episode .pt)         |
| Color shift / hue   | (manual; from Stage 3 MP4 review)              | —                | `tasks.json:per_date_notes.<date>.note`   |
| Occlusion           | (manual; from Stage 3 MP4 review)              | —                | same                                       |
| Audio-buffer drops  | (recorder logs at capture time)                | —                | recording-machine log                      |

---

## Quick "I'm adding one new date" runbook

```bash
DATE=2026-06-01
TASK=motherboard

# 1. integrity
python scripts/integrity_check.py data/$TASK/$DATE

# 2. compress
python -m twm.compress_h5 --in_dir data/$TASK/$DATE --in_place

# 3. preview MP4 (operator scrub)
python -m twm.visualize_projection data/$TASK/$DATE --save_videos

# 4. contact index → .pt
python -m twm.contact_index \
    --in_root  data/$TASK/$DATE \
    --out_root processed/mode1_v1/$TASK/$DATE

# 5. detect failure modes
python scripts/detect_failures.py --date $DATE

# 6. gallery GIFs
python scripts/make_episode_gifs_v3.py --only_date $DATE

# 7. per-event clips (only if Stage 5 flagged subtle stuff)
python scripts/inspect_short_freezes.py --only_date $DATE

# 8/9. edit tasks.json + README + docs/* by hand

# 10. push
HF_HUB_ENABLE_HF_TRANSFER=1 python scripts/push_hf.py
```

If any stage produces an unexpected count or a new failure mode, stop the
pipeline and write it up in `docs/caveats.md` before pushing.