Supported hardware¶

IP Cameras¶

OnVif compliant IP cameras (supporting the RTSP protocol)

Initial configuration of IP cameras can be a hurdle:

Many IP cameras typically require a half-broken Active-X (!) web-extension you have to download (to use with some outdated version of internet explorer).

Once you have sorted out those manufacturer-dependent issues you are good to go with OnVif and the RTSP protocol (as supported by libValkka).

Axis cameras, on the other hand, have a decent (standard javascript) web-interface for camera initial configuration.

Once you have done succesfull initial configuration, you can test the rtsp connection, with, say, using ffmpeg:

ffmpeg rtsp://user:password@ip-address

USB Cameras¶

USB Cameras capable of streaming H264

We have tested against Logitech HD Pro Webcam C920

Codecs¶

For the moment, the only supported codec is H264

Linux clients¶

libValkka uses OpenGL and OpenGL texture streaming, so it needs a robust OpenGL implementation. The current situation is:

Intel: the stock i915 driver is OK
Nvidia: use nvidia proprietary driver
ATI: not tested

OpenGL version 3 or greater is required. You can check your OpenGL version with the command glxinfo.

Hardware Acceleration¶

Please first read this word of warning.

VAAPI

Comes in the basic libValkka installation (and uses ffmpeg/libav infrastructure) - no additional packages needed.

First of all, the user using VAAPI, must belong to the “video” user group:

groups $USER
# run the following command if the user does not appear in the video group
sudo usermod -a -G video $USER
# after that you still need to logout & login

In order to use the VAAPI acceleration, just replace AVThread with VAAPIThread, i.e. instead of

avthread = AVThread("avthread", target_filter)

use this:

avthread = VAAPIThread("avthread", target_filter)

For more details about VAAPI, you can read this, this and this.

WARNING: VAAPI, especially the intel implementation, comes with a memory leak, which seems to be feature, not a bug - see discussions in here and here. I have confirmed this memory leak myself with libva 2.6.0.

The opensource Mesa implementation (“i965”) is (surprise!) more stable and libValkka enforces i965 internally by setting the environment variable LIBVA_DRIVER_NAME to i965 (this happens when you do from valkka.core import *).

If you really want to use other libva implementation, you can set

export VALKKA_LIBVA_DRIVER_NAME=your-driver-name

If you wish to use VAAPI in a docker environment, you should start docker with

--device=/dev/dri:/dev/dri

And be sure that the host machine has all required vaapi-related libraries installed (the easiest way: install libValkka on the host as well).

Finally, you can follow the GPU usage in realtime with:

sudo intel_gpu_top

NVidia / CUDA

Provided as a separate package that installs into the valkka.nv namespace and is used like this:

from valkka.nv import NVThread
avthread = NVThread("avthread", target_filter, gpu_index)

Available here

Huawei / CANN

Provided as a separate package. Very experimental and not guaranteed to work.