configure -enable-nonfree -enable-cuda-sdk -enable-libnpp -extra-cflags=-I/usr/local/cuda/include -extra-ldflags=-L/usr/local/cuda/lib64 I understand both CUDA Toolkit and Driver are installed as says the readme but when running. Using FFmpeg with NVIDIA GPU Hardware Acceleration :: NVIDIA Video Codec SDK Documentation So, before getting to solve the other issues in the Nano, I need to check that at least is able to encode video at the required speed.Īfter installing latest cmake from source, I follow the intructions I found here: In the case of the Vizi board, I have been able to compile ffmpeg to use Quick Sync, but the Atom is unable to reach my performance expectations (12FPS at 720p) due to the lack of power of the Atom GPU, and seems Quick Sync is unable to exploit Myriad X to encode video, thus no matter what I do to speed up inference, the truth is, I’m unable to encode fast enough with that board. I’m playiog with this topic with both a VIZI AI board (Atom + Myriad X) and the Jetson Nano 2GB board. I’m still struggling with my Jetson Nano 2GB board.Īfter desisting for now to make OpenCV work with CUDA (will recover this topic in the future), the next thing is to try to get ffmpeg to encode video using the CUDA magic.
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