Open Source Ecosystem

Qtechnology is a synonym of innovation and openness. We are a big player in the Open Source ecosystem with contributions in all the stack.

Even if you haven’t tried our products you are already using our software in your Phone, Browser or Cloud Provider. More than 1000 patches on the Linux kernel, GStreamer and OpenCV, among others, probe our devotion with Open Source.

Our cameras run the latest upstream software, tailored to their performance, so you simply have to focus on your area of expertise. No need to learn new libraries or fears of getting locked in a closed ecosystem.

The same software that can run on your notebook and make use of your webcam, can run on all our highly specialised cameras.

System: Yocto Project

We have decided to use Yocto Project / OpenEmbedded as the distribution for our cameras. With Yocto Project we provide thousands of libraries and applications tailored to the hardware of your camera, so you can get the most out of your camera.

Thanks to its hundreds of contributors and fast-paced development, our cameras are most likely able to solve your vision project out of the box.

Yocto Project, OpenEmbedded

Software Stack

There are multiple ways of developing your application. Qtechnology’s cameras give you complete freedom to choose the software stack that solves your computer vision problem. There is no need to build your applications on top of a proprietary library or application.

All the interaction with the camera and the FPGA is done through the standard Video4Linux API. This includes not only the different format converters but also the programming of the advanced components as the LUT, Camera Calibration or the Illumination Correction.

At Qtechnology, we can develop full application software for your application, from image processing to machine interfacing. We also have experience in integration with existing machinery from several projects .

For example, you can choose to build your app using:

  • Direct access to the camera via Video4Linux API, Eg: Qv4l2

  • Develop on top of the de-facto computer vision library OpenCV

  • Use one of the many Libraries from Python healthy Machine Learning/Computer Vision ecosystem, e.g: Scikit-learn or TensorFlow

  • Rely on GStreamer multimedia framework, to build your real-time video pipeline, with broadcasting quality
  • Leverage form any other third party library such as Halcon, MATLAB or GNU Octave

Hardware Acceleration

The access to the hardware acceleration is also done with a standard API, in this case, OpenCL, allowing your application to be ported seamlessly on all the different Qtechnology cameras, even on different architectures.

Nevertheless, we also support other parallel computing platforms as CUDA on our NVIDIA based cameras.

If you want to know more, feel free to contact us.