Eye tracker: one thread for OpenCV and numpy

Nothing called cv2.setNumThreads, so OpenCV kept a pool of one worker per core for
pupil windows of 140 to 240 px. Live, its idle workers spun and yielded about 14,000
times a second each, about a quarter of a core, next to SteamVR's compositor. numpy's
OpenBLAS also started 8 threads that never had work.

eyes_pupil.py now sets OpenCV to one thread, and ft-eyes sets OPENBLAS_NUM_THREADS and
OMP_NUM_THREADS to 1 before numpy loads (a value already in the environment wins).

Replaying fit1 into a scratch share (ft-eyes-replay, 14 s measured, capped at one core
with the replay): threads 13 -> 1, involuntary context switches 3,812/s -> 430/s,
system time 6.9% -> 1.6% of a core. Under that cap the frames it kept up with went from
21-36 to 57-69 a second per eye.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

gaze/README.md

@@ -63,7 +63,7 @@ Ground rules, for anyone changing it:
 - **Clean room.** Nothing of Valve's goes in: we don't decompile, disassemble, or patch the `eyetracking` binary or its network weights, and we don't copy their code or weights. Its public output (eye-server.mmap, read-only) is fair game as a baseline and as labels, and so are published papers and openly licensed pupil detectors (check each one's license: PuRe, PuReST, ElSe, and ExCuSe are non-commercial only).
 - **Root only reads.** ft-eyegrab never writes to, stops, or signals the `eyetracking` process, vrserver, or vrcompositor, never opens `/dev/adsp`, `/dev/cdsp`, or `/dev/spidev0.1`, and never writes to `/dev/shm/eye-server.mmap` (it also carries calibration clicks into SteamVR's tracker), `/opt`, or `/persist`.
 - **Eye images are biometric data.** Recordings live outside the repo, in `~/.local/share/frametop/eyes/captures` (0700), and `.gitignore` catches stray frame dumps. They go nowhere but the machine that runs your offline jobs.
-- **Mind the headset's budget.** Finding a pupil takes about 0.4 ms a frame while ft-eyes follows it, and 1.4-2.1 ms when it searches the whole frame. Replays, scoring, and training go to a PC.
+- **Mind the headset's budget.** Finding a pupil takes about 0.4 ms a frame while ft-eyes follows it, and 1.4-2.1 ms when it searches the whole frame. ft-eyes keeps OpenCV and numpy to one thread: their pools of one per core spun idle workers at about a quarter of a core, for frames this small. Replays, scoring, and training go to a PC.
 
 ## Headset fit