Support users to quickly setup and adjust the core concurrent video analysis workload through configuration file to obtain the best performance of video codec, post-processing and inference based on Intel® integrated GPU according to their product requirements. Users can use the sample application video_e2e_sample to complete runtime performance evaluation or as a reference for debugging core video workload issues.
Sample par files can be found in par_files directory. Verfied on i7-8559U. Performance differs on other platforms.
- 16 1080p H264 decoding, scaling, face detection inference, rendering inference results, composition, saving composition results to local H264 file, and display
- 4 1080p H264 decoding, scaling, human pose estimation inference, rendering inference results, composition and display
- 4 1080p H264 decoding, scaling, vehicle and vehicle attributes detection inference, rendering inference results, composition and display
- 16 1080p RTSP H264 stream decoding, scaling, face detection inference, rendering inference results, composition and display.
- 16 1080p H264 decoding, scaling, face detection inference, rendering inference results, composition and display. Plus 16 1080p H264 decoding, composition and showing on second display.
The sample application depends on Intel® Media SDK, Intel® OpenVINO™ and FFmpeg
See FAQ
The sample application is licensed under MIT license. See LICENSE for details.
See CONTRIBUTING for details. Thank you!
See user guide
Operating System:
- Ubuntu 18.04.02
Software:
Hardware:
- Intel® platforms supported by the MediaSDK 20.3.0 and OpenVINO 2021.1.
- For Media SDK, the major platform dependency comes from the back-end media driver. https://github.com/intel/media-driver
- For OpenVINO™, see details from here: https://software.intel.com/en-us/openvino-toolkit/documentation/system-requirements
Run build_and_install.sh to install dependent software packages and build sample application video_e2e_sample.
Please refer to ”Installation Guide“ in user guide for details.
Get sources with the following git command:
git clone https://github.com/intel-iot-devkit/concurrent-video-analytic-pipeline-optimization-sample-l.git cva_sample
cd cva_sample
./build_and_install.sh
This script will install the dependent software packages by running command "apt install". So it will ask for sudo password. Then it will download libva, libva-util, media-driver and MediaSDK source code and install these libraries. It might take 10 to 20 minutes depending on the network bandwidth.
After the script finishing, the sample application video_e2e_sample can be found under ./bin. Please refer to "Run sample application" in user guide for details.
The sample application has been validated on Intel® platforms Skylake(i7-6770HQ), Coffee Lake(i7-8559U i7-8700), Whiskey Lake(i7-8665UE) and Tiger Lake U(i7-1185G7E, i5-1135G7E).