alexei.dolgolyov
bb3a316e35
H8 — automations.ts rule-type registry
Convert the two hand-rolled RuleType dispatch ladders into per-type
registries (RULE_FIELD_RENDERERS + RULE_COLLECTORS) keyed by RuleType,
joining the existing RULE_CHIP_RENDERERS. All three are typed
Record<RuleType, ...> for compile-time exhaustiveness; an import-time
_assertRuleHandlerCoverage() check logs loudly if any registry drifts
from RULE_TYPE_KEYS — mirrors the backend's _RULE_HANDLERS shape, the
one intentional divergence being that the frontend logs rather than
throws (a thrown error at module import would brick the whole bundle,
not just the editor).
M7 — shared API client + 35 file migrations
New core/api-client.ts wrapping fetchWithAuth with typed apiGet /
apiPost / apiPut / apiPatch / apiDelete. Auth, 401-relogin, retry,
timeout, and the offline toast all stay owned by fetchWithAuth; the
client just collapses the
if (!resp.ok) { detail || HTTP <status> } ... resp.json()
dance into one typed call. The detail unwrap is hardened to join
FastAPI validation arrays instead of stringifying to [object Object].
35 feature/core files migrated to it across many batches, reviewer-
approved for behaviour parity in three passes covering the riskier
divergences (bulk Promise.allSettled deletes, inline-error saves,
array-detail joins, silent-failure GETs, immutable clones).
9 files remain on fetchWithAuth — the big god-modules tied to the
pending C8/C9/C10 splits (streams, settings, targets, dashboard,
color-strips/index, graph-editor, assets, value-sources) plus
pairing-flow which by design stays on raw fetch (branches on raw
Response.status codes).
i18n — 14 new locale keys (en / ru / zh)
Added save/load/delete error keys across automations, pattern,
audio_processing, audio_template, templates, gradient, target,
device namespaces, plus backfilled gradient.error.delete_failed into
ru/zh. Scan confirms no hardcoded English errorMessage strings
remain in the migrated diff.
AUDIT_REMAINING.md updated to reflect H6, H8, and M7 status.
Verified: tsc --noEmit clean + npm run build clean after every batch.
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.