Global provider of industrial vision components

GigE Vision camera simulator

GigESim is a component software solution that allows end-user and developers to turn their computer into a virtual GigE Vision camera. Any GigE Vision compliant software running at the same or other computers on the network will treat GigESim as an actual camera with fully adjustable features.

The package includes a powerful and easy-to-use GigE Server SDK that lets your applications stream images to multiple computers on the network and provides a full control over your application from remote locations. 

With GigESim you can:

  • Make your computer behave like a camera fully compatible with the GigE Vision and GenICam standards.
  • Select between GEV 1.2 and GEV 2.0 versions of the standard.
  • Create one or several virtual camera objects on one computer host.
  • Perform the development and testing of a client GigE Vision application when a real camera is not available.
  • Stream individual images or video sequences to the network and receive it at a remote node using standard GigE Vision software.
  • Utilize 1 Gbit and 10 Gbit network equipment.
  • Extend your throughput by using several network connections in parallel.
  • Stream GigE Vision video over Wi-Fi network.
  • Assign arbitrary IP and MAC addresses to your virtual cameras.
  • Transmit video data from each of your virtual cameras to multiple computers on the network in the multicast mode.
  • Convert an RGB color video to Bayer raw format to reduce the image payload size.
  • Select from dozens of monochrome and color pixel formats including packed ones.
  • Automatically compress ougoing image frames by utilizing built-in JPEG and H.264 encoders.
  • Add GenICam-compatible features to your virtual camera and control them from remote computers.
  • Send GenICam-compliant events from the virtual camera to remote clients on the message channel.
  • Receive action commands and scheduled action commands broadcasted by remote clients.
  • Select generated patterns, standard images (bmp, jpg, tif) or pre-recorded AVI files as a streaming video source.
  • Convert cameras of different types (analog, USB2, USB3, 1394, CameraLink) into simulated GigE Vision cameras.
  • Emulate dual-port GigE Vision cameras and network switches.
  • Implement distributed image processing on several computers using a parallel or/and pipeline architecture.
  • Implement a customized remote control over your application.
  • Prototype development and design of a GigE Vision camera .
  • Simulate rigorous network conditions by disordering and skipping video packets.
  • New! Compatibility with Windows 10 OS.
  • New! GEV 2.0 and 10 GigE connectivity support.
  • New! Support for IEEE-1588 Precision Time Protocol.
  • New! GigE Vision over Wi-Fi functionality.
  • New! Built-in JPEG and H.264 compression.
  • New! Linescan camera simulation mode.
  • New! Firewall traversing and packet size negotiation.
  • New! Support for exclusive and control-with-switchover access.
  • New! Ability to split video data into several stream channels.
  • New! Ability to integrate GenICam-compliant chunk data into video frames.
  • New! Control over camera response timing and inter-packet delay.
  • New! Universal GigE Vision pixel format convertor.
  • New! Certified compatibility with industry-standard GEV clients and hardware receivers.
  • New! Substantial performance increase due to multi-core and SSE code optimization.

  GigESim is an easy-to-use and flexible solution for camera prototyping, software testing, distributing computing, application remote control, multi-node surveillance, pipeline video transfer and many more.

GigESim can be used immediately, without adaptation. All you need is a Gigabit Ethernet or WiFi connection between your computers!
"This is one fun piece of software!"

Lorenz Blass,
Compar AG. Switzerland

"I would like to thank all GigESim developers. I have hardly seen such a good API before. Everything works fine, is more than well documented, easy to use and very easy to understand."

Denis Smirnov,
University of the Federal Arm Forces, Germany