wifi-densepose/scripts/pose-inference-proxy.README.md

pose-inference-proxy

Sidecar that plumbs model.safetensors inference into the sensing server's WebSocket broadcast so the dashboard renders real model output.

Why

The sensing server accepts --model <path> but the runtime broadcast loop emits pose_keypoints: None and keypoint confidences of 0.0 regardless of whether a model is loaded. The loaded weights are read once at startup for the Layer A ready log line and never fed into the broadcast path. This script fills the gap as a sidecar so the wiring can be observed and the dashboard can render real inference output without recompiling the Rust crate.

Run

pip install numpy safetensors websockets
huggingface-cli download ruvnet/wifi-densepose-pretrained \
  --local-dir models/wifi-densepose-pretrained

# 1. Start the sensing server normally
docker run -d -p 3000:3000 -p 3001:3001 ruvnet/wifi-densepose:latest

# 2. Start the proxy on a sibling port
python3 scripts/pose-inference-proxy.py \
  --model models/wifi-densepose-pretrained/model.safetensors \
  --upstream ws://localhost:3001/ws/sensing \
  --port 3002

# 3. Point the dashboard or any websocket client at the proxy port
#    ws://localhost:3002/ws/sensing
#    ws://localhost:3002/api/v1/stream/pose

What changes in the stream

Field	Upstream (port 3001)	Proxy (port 3002)
pose_keypoints	null	17 entries with real coordinates and presence sigmoid
classification.confidence	heuristic value	presence head sigmoid output
persons[0].keypoints[*].confidence	0.0	presence head sigmoid output
__model_inference__	absent	{model, presence_confidence, embedding_norm}
pose_source (on /api/v1/stream/pose)	signal_derived	model_inference

Caveats

• The published model.safetensors carries an encoder and a presence head. It does not carry a learned keypoint regressor. The proxy maps the embedding through a fixed Gaussian projection so the rendered skeleton reflects model state changes, but the per-joint positions are not from a trained pose head.
• The encoder accepts the sensing server's published simulation feed as well as real ESP32 CSI. Reported accuracy will reflect the input source.
• The script repairs the published safetensors header padding bug in-place on first load.