Skip to content
Documentation

OpenIPC Dashboard

OpenIPC Dashboard is a desktop VMS and analytics client for OpenIPC and ONVIF cameras. It is built with C++17, Qt 6, QML, GStreamer, and ONNX Runtime.

The project focuses on resilient RTSP playback, secure credential handling, transactional local state storage, and observable camera operations.

Main features

  • Low-latency RTSP streaming with Zero, Balanced, and Smooth buffering modes.
  • Hardware-accelerated video decoding with support for DXVA, D3D11, CUDA, and Intel QuickSync.
  • RTSP transport control over TCP, UDP, or HTTP.
  • Stream recovery with frame watchdogs, bounded reconnects, authentication-failure handling, and HD-to-SD fallback.
  • Coordinated manual and event recording with MP4 finalization and buffered evidence fallback.
  • Secure credential storage through the operating-system credential manager.
  • Versioned SQLite state with migration from legacy state.json.
  • Verified AI model downloads with SHA-256 and size checks.
  • OpenIPC / Majestic control center for runtime configuration, live ISP controls, metrics, reloads, backups, snapshots, and day/night hardware control.
  • Multi-layer camera discovery through OpenIPC mDNS, ONVIF WS-Discovery, Majestic and legacy WebUI fingerprints, RTSP/HTTP subnet probing, and Dahua SDK results.
  • Video mirroring for HUD or teleprompter use cases.

OpenIPC and Majestic control

Dashboard can open an OpenIPC / Majestic control center for OpenIPC cameras. It reads both /api/v1/config.json and the camera-specific /api/v1/config.schema.json, so the available settings follow the installed Majestic build.

Configuration writes are explicit: Dashboard prepares a minimal nested patch, shows a redacted diff, and only then posts the update to /api/v1/config. Older cameras without a schema can still be inspected, but schema-safe writes are not offered.

Camera discovery

Camera discovery combines multiple signals instead of relying on a single protocol. It can use OpenIPC mDNS markers, ONVIF WS-Discovery, Majestic and legacy WebUI fingerprints, bounded RTSP/HTTP subnet probing, and Dahua SDK results. Normal mode scans the local /24; deep mode can cover up to /20.

Build requirements

Development prerequisites from the upstream repository:

  • MSVC Visual Studio 2019+ or MinGW.
  • CMake 3.16 or newer.
  • Qt 6.4+ with Quick, Network, Multimedia, SQL, and Test modules.
  • GStreamer 1.x development and runtime packages.

Command-line build example:

Terminal window
mkdir build
cd build
cmake .. -DCMAKE_PREFIX_PATH="C:/Qt/6.x.x/msvc2019_64"
cmake --build .

System requirements

Minimum:

  • Windows 10 64-bit.
  • Intel Core i3 6th Gen, AMD Ryzen 3, or equivalent.
  • 4 GB RAM.
  • DirectX 11 capable GPU.
  • 100 Mbps Ethernet or 5 GHz Wi-Fi.

Recommended:

  • Windows 10/11 64-bit.
  • Intel Core i5, AMD Ryzen 5, or better.
  • 8 GB RAM or more.
  • Dedicated NVIDIA GPU with CUDA or Intel GPU with QuickSync for multi-stream hardware decoding.
  • Gigabit Ethernet for multiple high-bitrate streams.

Source