alexei.dolgolyov
e24f9d33cc
Two bugs caused user data ('G502' target's color-strip ref, etc.) to
revert after PC restart while persisting fine across normal app
restarts:
1. SQLite was in WAL mode with synchronous=NORMAL and Database.close()
was never called. On graceful Python exit the sqlite3 finalizer
checkpoints the WAL, but on an unclean PC shutdown (power loss,
forced reboot, or Windows force-terminating pythonw.exe) the WAL
stayed in OS cache, never reached disk, and the next boot rolled the
DB back to the last checkpoint -- losing recent edits.
2. Nothing handled WM_QUERYENDSESSION / WM_ENDSESSION, so on PC
shutdown Windows force-killed pythonw.exe after ~5s and the FastAPI
lifespan never ran. The 'stop_targets' setting was silently ignored
and devices were left at their last frame.
Changes:
- Database: PRAGMA synchronous=FULL + wal_autocheckpoint=100, plus an
explicit wal_checkpoint(TRUNCATE) inside Database.close().
- New utils/win_shutdown.py: hidden top-level window in a daemon thread
with a ctypes WindowProc that catches WM_QUERYENDSESSION (calls
ShutdownBlockReasonCreate to extend Windows' 5s hung-app timeout up
to the ~20s GUI ceiling), fires the shutdown callback, then waits in
WM_ENDSESSION on a completion event before returning. Also raises
the process shutdown priority via SetProcessShutdownParameters. All
Win32 argtypes/restypes are bound once at import to avoid LPARAM
overflow on x64.
- New shutdown_state.py: leaf module owning the cross-thread Event so
__main__ does not import the heavy ledgrab.main at startup.
- main.py lifespan: per-step asyncio.wait_for budgets (8s for
processor_manager.stop_all, 1.5s each for HA/MQTT, etc.) so a hung
device cannot starve the DB checkpoint, then db.close() and
shutdown_complete.set() always run.
- __main__.py: install the Windows shutdown guard before tray start;
install SIGINT/SIGTERM/SIGBREAK handlers only on the tray path
(uvicorn overwrites them on no-tray); raise server_thread.join to 20s.
- Tests cover WM_QUERYENDSESSION (fires callback, returns TRUE,
idempotent), WM_ENDSESSION (waits on event, times out cleanly,
cancel-path returns instantly), signal handler installation, and
that main and shutdown_state share the same Event instance.
LED Grab
Ambient lighting system that captures screen content and drives LED strips in real time. Supports WLED, Adalight, AmbileD, and DDP devices with audio-reactive effects, pattern generation, and automated profile switching.
What It Does
The server captures pixels from a screen (or Android device via ADB), extracts border colors, applies post-processing filters, and streams the result to LED strips at up to 60 fps. A built-in web dashboard provides device management, calibration, live LED preview, and real-time metrics — no external UI required.
A Home Assistant integration exposes devices as entities for smart home automation.
Features
Screen Capture
- Multi-monitor support with per-target display selection
- 6 capture engine backends — MSS (cross-platform), DXCam, BetterCam, Windows Graphics Capture (Windows), Scrcpy (Android via ADB), Camera/Webcam (OpenCV)
- Configurable capture regions, FPS, and border width
- Capture templates for reusable configurations
LED Device Support
- WLED (HTTP/UDP) with mDNS auto-discovery
- Adalight (serial) — Arduino-compatible LED controllers
- AmbileD (serial)
- DDP (Distributed Display Protocol, UDP)
- OpenRGB — PC peripherals (keyboard, mouse, RAM, fans, LED strips)
- Serial port auto-detection and baud rate configuration
Color Processing
- Post-processing filter pipeline: brightness, gamma, saturation, color correction, auto-crop, frame interpolation, pixelation, flip
- Reusable post-processing templates
- Color strip sources: audio-reactive, pattern generator, composite layering, audio-to-color mapping
- Pattern templates with customizable effects
Audio Integration
- Multichannel audio capture from any system device (input or loopback)
- WASAPI engine on Windows, Sounddevice (PortAudio) engine on Linux/macOS
- Per-channel mono extraction
- Audio-reactive color strip sources driven by frequency analysis
Automation
- Profile engine with condition-based switching (time of day, active window, etc.)
- Dynamic brightness value sources (schedule-based, scene-aware)
- Key Colors (KC) targets with live WebSocket color streaming
Dashboard
- Web UI at
http://localhost:8080— no installation needed on the client side - Progressive Web App (PWA) — installable on phones and tablets with offline caching
- Responsive mobile layout with bottom tab navigation
- Device management with auto-discovery wizard
- Visual calibration editor with overlay preview
- Live LED strip preview via WebSocket
- Real-time FPS, latency, and uptime charts
- Localized in English, Russian, and Chinese
Home Assistant Integration
- HACS-compatible custom component
- Light, switch, sensor, and number entities per device
- Real-time metrics via data coordinator
- WebSocket-based live LED preview in HA
Requirements
- Python 3.11+ (or Docker)
- A supported LED device on the local network or connected via USB
- Windows, Linux, or macOS — all core features work cross-platform
Platform Notes
| Feature | Windows | Linux / macOS |
|---|---|---|
| Screen capture | DXCam, BetterCam, WGC, MSS | MSS |
| Webcam capture | OpenCV (DirectShow) | OpenCV (V4L2) |
| Audio capture | WASAPI, Sounddevice | Sounddevice (PulseAudio/PipeWire) |
| GPU monitoring | NVIDIA (pynvml) | NVIDIA (pynvml) |
| Android capture | Scrcpy (ADB) | Scrcpy (ADB) |
| Monitor names | Friendly names (WMI) | Generic ("Display 0") |
| Profile conditions | Process/window detection | Not yet implemented |
Quick Start
Docker (recommended)
git clone https://git.dolgolyov-family.by/alexei.dolgolyov/ledgrab.git
cd ledgrab/server
docker compose up -d
Manual
Requires Python 3.11+ and Node.js 18+.
git clone https://git.dolgolyov-family.by/alexei.dolgolyov/ledgrab.git
cd ledgrab/server
# Build the frontend bundle
npm ci && npm run build
# Create a virtual environment and install
python -m venv venv
source venv/bin/activate # Linux/Mac
# venv\Scripts\activate # Windows
pip install .
# Start the server
export PYTHONPATH=$(pwd)/src # Linux/Mac
# set PYTHONPATH=%CD%\src # Windows
uvicorn ledgrab.main:app --host 0.0.0.0 --port 8080
Open http://localhost:8080 to access the dashboard.
Important: The default API key is
development-key-change-in-production. Change it before exposing the server outside localhost. See INSTALLATION.md for details.
See INSTALLATION.md for the full installation guide, including configuration, Docker manual builds, and Home Assistant setup.
Demo Mode
Demo mode runs the server with virtual devices, sample data, and isolated storage — useful for exploring the UI without real hardware.
Set the LEDGRAB_DEMO environment variable to true, 1, or yes:
# Docker
docker compose run -e LEDGRAB_DEMO=true server
# Python
LEDGRAB_DEMO=true uvicorn ledgrab.main:app --host 0.0.0.0 --port 8081
# Windows (installed app)
set LEDGRAB_DEMO=true
LedGrab.bat
Demo mode uses port 8081, config file config/demo_config.yaml, and stores data in data/demo/ (separate from production data). It can run alongside the main server.
Architecture
ledgrab/
├── server/ # Python FastAPI backend
│ ├── src/ledgrab/
│ │ ├── main.py # Application entry point
│ │ ├── config.py # YAML + env var configuration
│ │ ├── api/
│ │ │ ├── routes/ # REST + WebSocket endpoints
│ │ │ └── schemas/ # Pydantic request/response models
│ │ ├── core/
│ │ │ ├── capture/ # Screen capture, calibration, pixel processing
│ │ │ ├── capture_engines/ # MSS, DXCam, BetterCam, WGC, Scrcpy, Camera backends
│ │ │ ├── devices/ # WLED, Adalight, AmbileD, DDP, OpenRGB clients
│ │ │ ├── audio/ # Audio capture engines
│ │ │ ├── filters/ # Post-processing filter pipeline
│ │ │ ├── processing/ # Stream orchestration and target processors
│ │ │ └── profiles/ # Condition-based profile automation
│ │ ├── storage/ # JSON-based persistence layer
│ │ ├── static/ # Web dashboard (vanilla JS, CSS, HTML)
│ │ │ ├── js/core/ # API client, state, i18n, modals, events
│ │ │ ├── js/features/ # Feature modules (devices, streams, targets, etc.)
│ │ │ ├── css/ # Stylesheets
│ │ │ └── locales/ # en.json, ru.json, zh.json
│ │ └── utils/ # Logging, monitor detection
│ ├── config/ # default_config.yaml
│ ├── tests/ # pytest suite
│ ├── Dockerfile
│ └── docker-compose.yml
├── docs/
│ ├── API.md # REST API reference
│ └── CALIBRATION.md # LED calibration guide
├── INSTALLATION.md
└── LICENSE # MIT
Configuration
Edit server/config/default_config.yaml or use environment variables with the LEDGRAB_ prefix:
server:
host: "0.0.0.0"
port: 8080
log_level: "INFO"
auth:
api_keys:
dev: "development-key-change-in-production"
storage:
devices_file: "data/devices.json"
templates_file: "data/capture_templates.json"
logging:
format: "json"
file: "logs/ledgrab.log"
max_size_mb: 100
Environment variable override example: LEDGRAB_SERVER__PORT=9090.
API
The server exposes a REST API (with Swagger docs at /docs) covering:
- Devices — CRUD, discovery, validation, state, metrics
- Capture Templates — Screen capture configurations
- Picture Sources — Screen capture stream definitions
- Picture Targets — LED target management, start/stop processing
- Post-Processing Templates — Filter pipeline configurations
- Color Strip Sources — Audio, pattern, composite, mapped sources
- Audio Sources — Multichannel and mono audio device configuration
- Pattern Templates — Effect pattern definitions
- Value Sources — Dynamic brightness/value providers
- Key Colors Targets — KC targets with WebSocket live color stream
- Profiles — Condition-based automation profiles
All endpoints require API key authentication via X-API-Key header or ?token= query parameter.
See docs/API.md for the full reference.
Calibration
The calibration system maps screen border pixels to physical LED positions. Configure layout direction, start position, and per-edge segments through the web dashboard or API.
See docs/CALIBRATION.md for a step-by-step guide.
Home Assistant
For Home Assistant integration, see the separate ledgrab-haos-integration repository.
Development
cd server
# Install with dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Format and lint
black src/ tests/
ruff check src/ tests/
Optional extras:
pip install -e ".[perf]" # High-performance capture engines (Windows)
pip install -e ".[camera]" # Webcam capture via OpenCV
License
MIT — see LICENSE.