OpenCV Calibration WebApp Module

This repository contains a small Viam module that exposes calibration files stored on the robot filesystem. The service implements the Generic service API and accepts two do_command calls:

Command	Payload	Description
list_passes	{ "command": "list_passes" }	Returns all pass folders and files.
get_file	{ "command": "get_file", "pass_id": "...", "filename": "..." }	Returns one file (base64 encoded).
get_base_dir	{ "command": "get_base_dir" }	Returns the absolute path the service watches.

The repository also includes static assets (index.html, style.css, main.ts) that you can bundle into a separate Viam application if you want a hosted UI. They are not required for the module itself.

Repository layout

opencv-webapp/
├── opencv_webapp/
│   ├── __init__.py
│   └── webapp.py                # Generic service implementation
├── example-config.json          # Sample robot configuration snippet
├── index.html / style.css / main.ts  # Optional frontend assets
├── meta.json                    # Module manifest (build + entrypoint)
├── requirements.txt             # Python dependencies
├── run.sh                       # Entrypoint used by viam-server
└── Makefile                     # Helper targets for packaging

Packaging & build

The module vendors its Python dependencies into a local lib/ directory during build. The Makefile hides the details:

make build        # installs deps into lib/ and creates module.tar.gz
make clean        # removes lib/ and the tarball

meta.json references these targets so viam module reload --cloud-build works out of the box.

Deploying to a robot

1. Push this repository to a git host the Viam builder can access.

2. In the Viam console, add a Module → Local entry pointing to this repo.

3. Add a Generic service resource:

   {
     "name": "webapp",
     "type": "generic",
     "namespace": "viam",
     "model": "viam:opencv-webapp:webapp",
     "attributes": {
       "assets_dir": "./module-data/calibration-passes"
     }
   }

   Change assets_dir if your passes live elsewhere.

4. After deployment, the service responds to do_command from any Viam SDK client (including the optional frontend in this repo).

Local testing

# Create some fake data
mkdir -p module-data/calibration-passes/test-pass
echo "example" > module-data/calibration-passes/test-pass/data.txt
touch module-data/calibration-passes/test-pass/.complete

# Invoke service locally
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python - <<'PY'
from opencv_webapp.webapp import WebApp
import asyncio

async def main():
    svc = WebApp("./module-data/calibration-passes")
    print(await svc.do_command({"command": "list_passes"}))

asyncio.run(main())
PY

Optional frontend

index.html, style.css, and main.ts can be bundled (for example with esbuild or Vite) and hosted via Viam Applications.

The frontend now discovers credentials automatically:

• When deployed through Viam Applications, the runtime injects window.__VIAM_WEB_APP__; the script reads the machine host, service name, and any referenced secrets from there.
• For local development, edit the JSON block inside index.html’s <script id="viam-web-app-config">…</script> tag. Replace the placeholder host/API key with your real values before building.
• Ensure the serviceName matches the Generic service name configured on your machine (for example webapp or calibration-service).

License

Apache-2.0