Python tool based on ffmpeg and ffprobe to deal with the video preprocesing required for VMAF inputs:
- Deinterlacing
- Upscaling/downscaling
- Frame-to-Frame Syncing
- Frame rate adaptation
Details about How it Works can be found here.
Since easyVmaf 2.0 only FFmpeg versions >= 5.0 will be supported. For using easyVmaf with FFmpeg < 5.0, please consider rollingback to easyVmaf 1.3.
New feaures and updates:
-
Cambi feature - Netflix banding detector, supported.
-
Command line ussage updated according to libvmaf docs
-
Cambi heatmap support added. The outputs may be visualized with ffplay
-
Built-in VMAF models are only supported since they are included in FFmpeg >=
v5.0. -
Docker image - better handling of dependencies and built instruccions
-
'HD Neg' and 'HD phone' models are computed by default
-
Linux/OSX -
Python
>= v3.0 -
Python module ffmpeg_progress_yield
-
FFmpeg >=
5.0build withlibvmaf. More details here
- Just clone the repo and run it from the source folder.
$ git clone https://github.com/gdavila/easyVmaf.git
$ cd easyVmaf- Run from docker image. More info at the end of this document.
$ python3 easyVmaf.py
usage: easyVmaf [-h] -d D -r R [-sw SW] [-ss SS] [-fps FPS] [-subsample N] [-reverse] [-model MODEL]
[-threads THREADS] [-verbose] [-progress] [-endsync] [-output_fmt OUTPUT_FMT]
[-cambi_heatmap]
Script to easy compute VMAF using FFmpeg. It allows to deinterlace, scale and sync Ref and Distorted video samples automatically:
Autodeinterlace: If the Reference or Distorted samples are interlaced, deinterlacing is applied
Autoscale: Reference and Distorted samples are scaled automatically to 1920x1080 or 3840x2160 depending on the VMAF model to use
Autosync: The first frames of the distorted video are used as reference to a sync look up with the Reference video.
The sync is doing by a frame-by-frame look up of the best PSNR
See [-reverse] for more options of syncing
As output, a json file with VMAF score is created
optional arguments:
-h, --help show this help message and exit
-sw SW Sync Window: window size in seconds of a subsample of the Reference video. The sync lookup will be done between the first frames of the Distorted input and this Subsample of the Reference. (default=0. No sync).
-ss SS Sync Start Time. Time in seconds from the beginning of the Reference video to which the Sync Window will be applied from. (default=0).
-fps FPS Video Frame Rate: force frame rate conversion to <fps> value. Autodeinterlace is disabled when setting this
-subsample N Specifies the subsampling of frames to speed up calculation. (default=1, None).
-reverse If enable, it Changes the default Autosync behaviour: The first frames of the Reference video are used as reference to sync with the Distorted one. (Default = Disable).
-model MODEL Vmaf Model. Options: HD, 4K. (Default: HD).
-threads THREADS number of threads
-verbose Activate verbose loglevel. (Default: info).
-progress Activate progress indicator for vmaf computation. (Default: false).
-endsync Activate end sync. This ends the computation when the shortest video ends. (Default: false).
-output_fmt OUTPUT_FMT
Output vmaf file format. Options: json or xml (Default: json)
-cambi_heatmap Activate cambi heatmap. (Default: false).
-sync_only Sync measurement only. No Vmaf processing (Default: false).
required arguments:
-d D Distorted video
-r R Reference video 
VMAF computation for two video samples, reference.ts and distorted-A.ts. Both videos are not synced: reference.ts is delayed in comparition with distorted-A.ts, i.e., the first frame of distorted-A.ts matchs with the frame located at 0.7007 seconds since the begining of reference.ts (blue arrow on the figure). To sync both videos automatically using easyVmaf, the next command line is used:
```bash
$ python3 easyVmaf.py -d distorted-A.ts -r reference.ts -sw 2
...
...
[Ignored outputs]
...
...
Sync Info:
offset: 0.7007000000000001 psnr: 48.863779
VMAF score: 89.37913542219542
VMAF json File Path: distorted-A_vmaf.json
```
The previus command line takes a synchronisation window sw of 2 seconds , this means that the sync lookup will be done between the first frame of distorted-A.ts (actually, in practise it takes into account several frames) and a subsample of reference.ts of 2 seconds lenght since its begin.
```bash
$ python3 easyVmaf.py -d distorted.ts -r reference.ts -sw 2
...
...
[Ignored FFmpeg outputs]
...
...
Sync Info:
offset: 0.7007000000000001 psnr: 48.863779
VMAF score: 89.37913542219542
VMAF json File Path: distorted.json
```
This time, distorted-B.ts is delayed in comparition with reference.ts, i.e., The first frame of reference.ts matchs with the frame located at 8.3003 seconds since the begining of distorted-B.ts. To sync the videos automatically, the next command line is used:
```bash
$ python3 easyVmaf.py -d distorted-B.ts -r reference.ts -sw 3 -ss 6 -reverse
...
...
[Ignored FFmpeg outputs]
...
...
Sync Info:
offset: 8.300300000000000 psnr: 34.897866
VMAF score: 92.34452778643345
VMAF json File Path: distorted-B_vmaf.json
```
The previous command line applies a syncronization window sw of 3 seconds, a sync start time ss of 6 seconds and the reverse flag.
Note the use of the reverse flag (that was not used on the first example). This flag allows to interchange to which video the syncWindow will be applied (reference or distorted).
A docker image is available on docker hub to run easyVmaf in a straightforward way.
The Docker Image is basically an ubuntu image with ffmpeg and libvmaf already installed. You can check the Dockerfile for more details.
The easiest way to run easyVmaf through Docker is mounting a shared volume between your host machine and the container. This volume should have inside it all the video files you want to analyze. The outputs (vmaf information files) will be putting in this shared folder also.
Example
docker run --rm -v <local-path-to-your-video-files>:/<custom-name-folder> gfdavila/easyvmaf -r /<custom-name-folder>/video-1.mp4 -d /<custom-name-folder>/video-2.mp4Some video samples located on the docker image:
NAME TIME
t=0
|
BBB_reference_10s.mp4 */-----------------------------*/
BBB_sampleA_distorted.mp4 */---------------------*/
BBB_sampleB_distorted.mp4 */-------------------------*/
Run docker container to get VMAF between BBB_reference_10s.mp4 and BBB_sampleA_distorted.mp4:
:~$ docker run --rm gfdavila/easyvmaf -r video_samples/BBB_reference_10s.mp4 -d video_samples/BBB_sampleA_distorted.mp4 -sw 1 -ss 1Run docker container to get VMAF between BBB_sampleA_distorted.mp4 and BBB_sampleB_distorted.mp4:
:~$ docker run --rm gfdavila/easyvmaf -r video_samples/BBB_sampleA_distorted.mp4 -d video_samples/BBB_sampleB_distorted.mp4 -sw 2 -ss 0 -reverse