Metadata-Version: 2.1
Name: ffmpeg-quality-metrics
Version: 0.9.0
Summary: Calculate quality metrics with FFmpeg (SSIM, PSNR, VMAF)
Home-page: https://github.com/slhck/ffmpeg-quality-metrics
Author: Werner Robitza
Author-email: werner.robitza@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Multimedia :: Video
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: pandas

FFmpeg Quality Metrics
======================

|PyPI version|

Simple script for calculating quality metrics with FFmpeg.

Currently supports PSNR, SSIM and VMAF. It will output:

-  the per-frame metrics
-  metrics for each component (Y, U, V)
-  global statistics (min/max/average/standard deviation)

Author: Werner Robitza werner.robitza@gmail.com

Contents:

-  `Requirements <#requirements>`__
-  `Installation <#installation>`__
-  `Usage <#usage>`__
-  `Running with Docker <#running-with-docker>`__
-  `Output <#output>`__
-  `License <#license>`__

--------------

Requirements
------------

-  Python 3.6 or higher
-  FFmpeg:

   -  **Linux:** Download the git master build from
      `here <https://johnvansickle.com/ffmpeg/>`__. Installation
      instructions, as well as how to add FFmpeg and FFprobe to your
      PATH, can be found
      `here <https://www.johnvansickle.com/ffmpeg/faq/>`__.
   -  **macOS:** Download the *snapshot* build from
      `here <https://evermeet.cx/ffmpeg/>`__.
   -  **Windows:** Download an FFmpeg binary from
      `here <https://www.gyan.dev/ffmpeg/builds/>`__. The
      ``git essentials`` build will suffice.

Put the ``ffmpeg`` executable in your ``$PATH``.

*FFmpeg can be installed using Homebrew, but it is recommended that you
use one of the FFmpeg builds linked above, otherwise libvmaf <v2.0.0
will be used, which is ~2x slower
(*\ `source <https://netflixtechblog.com/toward-a-better-quality-metric-for-the-video-community-7ed94e752a30>`__\ *).*

Installation
------------

::

   pip3 install ffmpeg_quality_metrics

Or clone this repository, then run the tool with
``python3 -m ffmpeg_quality_metrics``

Usage
-----

In the simplest case, if you have a distorted (encoded, maybe scaled)
version and the reference:

::

   ffmpeg_quality_metrics distorted.mp4 reference.avi

The distorted file will be automatically scaled to the resolution of the
reference.

Extended Options
~~~~~~~~~~~~~~~~

See ``ffmpeg_quality_metrics -h``:

::

   usage: [-h] [-n] [-v] [-ev] [-m MODEL_PATH] [-p] [-dp]
          [-s {fast_bilinear,bilinear,bicubic,experimental,neighbor,area,bicublin,gauss,sinc lanczos,spline}]
          [-of {json,csv}] [-r FRAMERATE] [-t THREADS] [-nt N_THREADS]
          dist ref

   positional arguments:
     dist                  input file, distorted
     ref                   input file, reference

   optional arguments:
     -h, --help            show this help message and exit
     -n, --dry-run         Do not run command, just show what would be done (default: False)
     -v, --verbose         Show verbose output (default: False)
     -ev, --enable-vmaf    Enable VMAF computation; calculates VMAF as well as SSIM and PSNR (default: False)
     -m MODEL_PATH, --model-path MODEL_PATH
                           Specify the path of the VMAF model file (default: None)
     -p, --phone-model     Enable VMAF phone model (default: False)
     -dp, --disable-psnr-ssim
                           Disable PSNR/SSIM computation. Use VMAF to get YUV estimate. (default: False)
     -s {fast_bilinear,bilinear,bicubic,experimental,neighbor,area,bicublin,gauss,sinc,lanczos,spline}, --scaling-algorithm {fast_bilinear,bilinear,bicubic,experimental,neighbor,area,bicublin,gauss,sinc,lanczos,spline}
                           Scaling algorithm for ffmpeg (default: bicubic)
     -of {json,csv}, --output-format {json,csv}
                           Output format for the metrics (default: json)
     -r FRAMERATE, --framerate FRAMERATE
                           Force an input framerate (default: None)
     -t THREADS, --threads THREADS
                           Number of threads to do the calculations (default: 0)
     -nt N_THREADS, --n-threads N_THREADS
                           Set the value of libvmaf's n_threads option. This determines the number of threads that are used for VMAF calculation

Specifying VMAF Model
~~~~~~~~~~~~~~~~~~~~~

Use the ``-m/--model-path`` option to set the path to the model file.

For example, if you have the model file saved at:

::

   /usr/local/opt/libvmaf/share/model/vmaf_v0.6.1.json

Run the command with:

::

   ffmpeg_quality_metrics dist.mkv ref.mkv -m /usr/local/opt/libvmaf/share/model/vmaf_v0.6.1.json

Running with Docker
-------------------

If you don’t want to deal with dependencies, build the image with
Docker:

::

   docker build -t ffmpeg_quality_metrics .

This installs ``ffmpeg`` with all dependencies. You can then run the
container, which basically calls the Python script. To help you with
mounting the volumes (since your videos are not stored in the
container), you can run a helper script:

::

   ./docker_run.sh

Check the output of the above command for more help.

Output
------

JSON or CSV, including individual fields for Y, U, V, and averages, as
well as frame numbers.

JSON example:

::

   ➜ ffmpeg_quality_metrics test/dist-854x480.mkv test/ref-1280x720.mkv --enable-vmaf
   {
       "vmaf": [
           {
               "adm2": 0.69908,
               "motion2": 0.0,
               "ms_ssim": 0.89698,
               "psnr": 18.58731,
               "ssim": 0.92415,
               "vif_scale0": 0.53962,
               "vif_scale1": 0.71805,
               "vif_scale2": 0.75205,
               "vif_scale3": 0.77367,
               "vmaf": 14.07074,
               "n": 1
           },
           {
               "adm2": 0.69846,
               "motion2": 0.35975,
               "ms_ssim": 0.89806,
               "psnr": 18.60299,
               "ssim": 0.9247,
               "vif_scale0": 0.54025,
               "vif_scale1": 0.71961,
               "vif_scale2": 0.75369,
               "vif_scale3": 0.77607,
               "vmaf": 14.48034,
               "n": 2
           },
           {
               "adm2": 0.69715,
               "motion2": 0.35975,
               "ms_ssim": 0.89879,
               "psnr": 18.6131,
               "ssim": 0.92466,
               "vif_scale0": 0.5391,
               "vif_scale1": 0.71869,
               "vif_scale2": 0.75344,
               "vif_scale3": 0.77616,
               "vmaf": 14.27326,
               "n": 3
           }
       ],
       "psnr": [
           {
               "n": 1,
               "mse_avg": 536.71,
               "mse_y": 900.22,
               "mse_u": 234.48,
               "mse_v": 475.43,
               "psnr_avg": 20.83,
               "psnr_y": 18.59,
               "psnr_u": 24.43,
               "psnr_v": 21.36
           },
           {
               "n": 2,
               "mse_avg": 535.29,
               "mse_y": 896.98,
               "mse_u": 239.4,
               "mse_v": 469.49,
               "psnr_avg": 20.84,
               "psnr_y": 18.6,
               "psnr_u": 24.34,
               "psnr_v": 21.41
           },
           {
               "n": 3,
               "mse_avg": 535.04,
               "mse_y": 894.89,
               "mse_u": 245.8,
               "mse_v": 464.43,
               "psnr_avg": 20.85,
               "psnr_y": 18.61,
               "psnr_u": 24.22,
               "psnr_v": 21.46
           }
       ],
       "ssim": [
           {
               "n": 1,
               "ssim_y": 0.934,
               "ssim_u": 0.96,
               "ssim_v": 0.942,
               "ssim_avg": 0.945
           },
           {
               "n": 2,
               "ssim_y": 0.934,
               "ssim_u": 0.96,
               "ssim_v": 0.943,
               "ssim_avg": 0.946
           },
           {
               "n": 3,
               "ssim_y": 0.934,
               "ssim_u": 0.959,
               "ssim_v": 0.943,
               "ssim_avg": 0.945
           }
       ],
       "global": {
           "ssim": {
               "average": 0.9453333333333332,
               "stdev": 0.00047140452079103207,
               "min": 0.945,
               "max": 0.946
           },
           "psnr": {
               "average": 20.84,
               "stdev": 0.008164965809278536,
               "min": 20.83,
               "max": 20.85
           },
           "vmaf": {
               "average": 14.27478,
               "stdev": 0.16722195390159322,
               "min": 14.07074,
               "max": 14.48034
           }
       },
       "input_file_dist": "test/dist-854x480.mkv",
       "input_file_ref": "test/ref-1280x720.mkv"
   }

CSV example:

::

   ➜ ffmpeg_quality_metrics test/dist-854x480.mkv test/ref-1280x720.mkv --enable-vmaf -of csv
   n,adm2,motion2,ms_ssim,psnr,ssim,vif_scale0,vif_scale1,vif_scale2,vif_scale3,vmaf,mse_avg,mse_u,mse_v,mse_y,psnr_avg,psnr_u,psnr_v,psnr_y,ssim_avg,ssim_u,ssim_v,ssim_y,input_file_dist,input_file_ref
   1,0.70704,0.0,0.89698,18.58731,0.92415,0.53962,0.71805,0.75205,0.77367,15.44212,536.71,234.48,475.43,900.22,20.83,24.43,21.36,18.59,0.945,0.96,0.942,0.934,test/dist-854x480.mkv,test/ref-1280x720.mkv
   2,0.7064,0.35975,0.89806,18.60299,0.9247,0.54025,0.71961,0.75369,0.77607,15.85038,535.29,239.4,469.49,896.98,20.84,24.34,21.41,18.6,0.946,0.96,0.943,0.934,test/dist-854x480.mkv,test/ref-1280x720.mkv
   3,0.70505,0.35975,0.89879,18.6131,0.92466,0.5391,0.71869,0.75344,0.77616,15.63546,535.04,245.8,464.43,894.89,20.85,24.22,21.46,18.61,0.945,0.959,0.943,0.934,test/dist-854x480.mkv,test/ref-1280x720.mkv

License
-------

ffmpeg_quality_metrics, Copyright (c) 2019 Werner Robitza

Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
“Software”), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:

The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

.. |PyPI version| image:: https://badge.fury.io/py/ffmpeg_quality_metrics.svg
   :target: https://badge.fury.io/py/ffmpeg_quality_metrics


