Metadata-Version: 1.1
Name: ffmpeg_quality_metrics
Version: 0.11.0
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
Description: 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>`__
        -  `API <#api>`__
        -  `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 takes a few minutes and installs the latest ``ffmpeg`` `as a static
        build <https://johnvansickle.com/ffmpeg/>`__ with libvmaf 2.x.
        
        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 <dist> <ref> [OPTIONS]
        
        Check the output of ``./docker_run.sh`` for more help.
        
        For example, to run the tool with the bundled test videos and enable
        VMAF calculation:
        
        ::
        
           ./docker_run.sh test/dist-854x480.mkv test/ref-1280x720.mkv -ev
        
        For Homebrew ffmpeg, a ``Dockerfile-legacy`` is provided.
        
        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
        
        API
        ---
        
        The program exposes an API that you can use yourself:
        
        .. code:: python
        
           from ffmpeg_quality_metrics.ffmpeg_quality_metrics import FfmpegQualityMetrics as ffqm
        
           ffqm("path/to/ref", "path/to/dist").calc_ssim_psnr()
        
        For more usage please read `the docs <docs/index.html>`__.
        
        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
