Metadata-Version: 1.1
Name: ffmpeg-quality-metrics
Version: 0.3.11
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
Description: FFmpeg Quality Metrics
        ======================
        
        |PyPI version|
        
        Simple script for calculating quality metrics with FFmpeg.
        
        Currently supports PSNR, SSIM and VMAF.
        
        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:
        
           -  download a static build from `their
              website <http://ffmpeg.org/download.html>`__)
           -  put the ``ffmpeg`` executable in your ``$PATH``
        
        Optionally, you may install FFmpeg with ``libvmaf`` support to run VMAF
        score calculation. Under Linux and macOS, this can be done with the
        following steps:
        
        -  Install `Homebrew <https://brew.sh/>`__
        -  Install `this
           tap <https://github.com/homebrew-ffmpeg/homebrew-ffmpeg/>`__
        -  Run ``brew install ffmpeg --with-libvmaf``.
        
        Under Windows, you have to install ffmpeg and VMAF manually, or using
        `helper
        scripts <https://github.com/rdp/ffmpeg-windows-build-helpers>`__.
        
        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.
        
        See ``ffmpeg_quality_metrics -h``:
        
        ::
        
           usage: ffmpeg_quality_metrics [-h] [-n] [-v] [-ev] [-m MODEL_PATH] [-p]
                                            [-dps]
                                            [-of {json,csv}]
                                            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
                                   Set path to VMAF model file (.pkl) (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, --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 in which format (default: 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": 0.70704,
                       "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": 15.44212,
                       "n": 1
                   },
                   {
                       "adm2": 0.7064,
                       "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": 15.85038,
                       "n": 2
                   },
                   {
                       "adm2": 0.70505,
                       "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": 15.63546,
                       "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
                   }
               ],
               "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
        
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
