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VMAF Usage through Docker

Install Docker, then, from the VMAF directory, run:

docker build -t vmaf .

And to use it, just run:

docker run --rm vmaf [CLI]

Where [CLI] is one of run_vmaf.

For example, if you are under root, to run run_vmaf on a sample reference/distorted video pair under resource/yuv:

docker run --rm -v $(pwd):/files vmaf \
    yuv420p 576 324 \
    /files/src01_hrc00_576x324.yuv \
    /files/src01_hrc01_576x324.yuv \
    --out-fmt json

Note that you need to first download the test videos from vmaf_resource.

wget https://github.com/Netflix/vmaf_resource/raw/master/python/test/resource/yuv/src01_hrc00_576x324.yuv
wget https://github.com/Netflix/vmaf_resource/raw/master/python/test/resource/yuv/src01_hrc01_576x324.yuv

Docker with CUDA support

To run docker containers with GPU support you have to install the nvidia container toolkit. After that a CUDA enabled container can be built using the below command line:

docker build -f Dockerfile.cuda -t vmaf_cuda .

Besides VMAF the container also build ffmpeg support to enable GPU enabled decode to be able to run VMAF at speed of light.

REF_VIDEO=$PWD/data/text_ref.mp4 
DIS_VIDEO=$PWD/data/text_dis.mp4 

docker run --gpus all  -e NVIDIA_DRIVER_CAPABILITIES=compute,video -v $REF_VIDEO:/data/ref.mp4 -v $DIS_VIDEO:/data/dis.mp4 vmaf_cuda

To run a custom ffmpeg command line inside the container use:

docker run --gpus all -e NVIDIA_DRIVER_CAPABILITIES=compute,video --entrypoint=bash -it --rm vmaf_cuda

For 420 video format we will have to convert from NV12 to 420 as well: -filter_complex [0:v]scale_cuda=format=yuv420p[ref];[1:v]scale_cuda=format=yuv420p[dist];[dist][ref]libvmaf_cuda