feat(data-only-viz): action-head review TUI

Add interactive console TUI for manual label review of
auto-labeled action datasets. Displays ASCII skeleton,
kinetics, and proposed label with confidence. User can
accept proposed label, choose manual override (1/2/3),
skip, or quit. Reads auto-labeled JSONL and writes
validated rows to reviewed dataset.

data_only_viz/training/review.py

@@ -0,0 +1,116 @@
+"""Manual label review TUI.
+
+Reads an auto-labeled jsonl dataset, presents each window with:
+  - ASCII skeleton (front view) of last frame
+  - speed/accel/sym kinetics
+  - proposed label + confidence
+Keys:
+  1 = debout, 2 = assise, 3 = danse
+  ENTER = accept proposed label
+  S = skip (label = None, will not be saved)
+  Q = quit and write what we have so far
+
+Usage:
+    uv run python -m data_only_viz.training.review \\
+        --in ~/.cache/av-live-action/dataset/auto.jsonl \\
+        --out ~/.cache/av-live-action/dataset/reviewed.jsonl
+"""
+from __future__ import annotations
+
+import argparse
+import sys
+import termios
+import tty
+from pathlib import Path
+
+import numpy as np
+
+from data_only_viz.action_head import LABELS
+from data_only_viz.training.autolabel import autolabel_window
+from data_only_viz.training.dataset import (
+    DatasetRow,
+    load_dataset_jsonl,
+    write_dataset_jsonl,
+)
+
+
+def _ascii_skeleton(j3d: np.ndarray, width: int = 40, height: int = 16) -> str:
+    pts = j3d[:, [0, 1]]  # x, y
+    mn = pts.min(axis=0)
+    mx = pts.max(axis=0)
+    rng = np.maximum(mx - mn, 1e-3)
+    norm = (pts - mn) / rng
+    grid = [[" "] * width for _ in range(height)]
+    for x, y in norm:
+        col = int(x * (width - 1))
+        row = int((1 - y) * (height - 1))
+        grid[row][col] = "*"
+    return "\n".join("".join(row) for row in grid)
+
+
+def _getch() -> str:
+    fd = sys.stdin.fileno()
+    old = termios.tcgetattr(fd)
+    try:
+        tty.setraw(fd)
+        return sys.stdin.read(1)
+    finally:
+        termios.tcsetattr(fd, termios.TCSADRAIN, old)
+
+
+def review(in_path: Path, out_path: Path,
+           sample_validated_fraction: float = 0.2,
+           seed: int = 0) -> None:
+    from data_only_viz.action_head import FeatureExtractor
+
+    rows = load_dataset_jsonl(in_path)
+    rng = np.random.default_rng(seed)
+    kept: list[DatasetRow] = []
+    for i, r in enumerate(rows):
+        proposed, conf = autolabel_window(list(r.j3d_stack))
+        is_none = proposed is None
+        sampled = rng.random() < sample_validated_fraction
+        if not is_none and not sampled and r.manually_validated:
+            kept.append(r)
+            continue
+        print("\033[2J\033[H")  # clear
+        print(f"[{i + 1}/{len(rows)}] {r.window_id}  proposed={proposed} conf={conf:.2f}")
+        print(_ascii_skeleton(r.j3d_stack[-1]))
+        kin = FeatureExtractor.kinetics(list(r.j3d_stack))
+        print(f"speed={kin[0]:.3f}  accel={kin[1]:.3f}  sym={kin[2]:+.3f}")
+        print("keys: 1=debout 2=assise 3=danse ENTER=accept S=skip Q=quit")
+        k = _getch().lower()
+        if k == "q":
+            break
+        if k == "s":
+            continue
+        if k == "\r":
+            chosen = proposed
+        elif k in ("1", "2", "3"):
+            chosen = LABELS[int(k) - 1]
+        else:
+            continue
+        if chosen is None:
+            continue
+        kept.append(DatasetRow(
+            window_id=r.window_id, label=chosen, j3d_stack=r.j3d_stack,
+            session=r.session, pid_local=r.pid_local,
+            auto_label_confidence=conf, manually_validated=True,
+        ))
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    write_dataset_jsonl(kept, out_path)
+    print(f"\nwrote {len(kept)} rows to {out_path}")
+
+
+def _cli() -> None:
+    p = argparse.ArgumentParser()
+    p.add_argument("--in", dest="in_path", required=True, type=Path)
+    p.add_argument("--out", dest="out_path", required=True, type=Path)
+    p.add_argument("--sample-fraction", type=float, default=0.2)
+    args = p.parse_args()
+    review(args.in_path, args.out_path,
+           sample_validated_fraction=args.sample_fraction)
+
+
+if __name__ == "__main__":
+    _cli()