Operator clarified: nodes 1 and 2 (.101 / .100) are ESP32-S3 + OV-camera
boards (sensor_06, sensor_07 in the photo set), NOT YD-ESP32-23. Nodes
3-6 (.102 / .104 / .105 / .106) are the YD-ESP32-23 boards with u.FL
external-antenna connectors (sensor_08, sensor_09).
Impact: Pack E.2 (WiFlow camera-supervised retrain) is closer than
previously assumed — the camera hardware is already deployed at nodes
1 and 2. Path becomes:
1. Extend FW with parallel camera_capture.c → stream MJPEG over UDP/HTTP
2. Run MediaPipe Pose on server (deps already installed in
~/.venv/ruview-train from earlier session)
3. Time-align with existing scripts/align-ground-truth.js
4. Retrain via scripts/train-wiflow-supervised.js --scale lite
The 4 PCB-strip antennas in sensor_02 map 1:1 to nodes 3-6 — drop-in
upgrade once each board is power-cycled to swap the antenna feed.
README now lists the per-node board type, IP, camera/u.FL status, and
which photos show each. No code changes.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads.
- Added a comprehensive training utility for the model, including loss functions and training steps.
- Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
arsen
rUv