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• avfilter/dnn : Refactor DNN parameter configuration system

  7 mai 2024, par Zhao Zhili

  avfilter/dnn : Refactor DNN parameter configuration system
  This patch trying to resolve mulitiple issues related to parameter
  
  configuration :
  Firstly, each DNN filters duplicate DNN_COMMON_OPTIONS, which should
  
  be the common options of backend.
  Secondly, backend options are hidden behind the scene. It's a
  
  AV_OPT_TYPE_STRING backend_configs for user, and parsed by each
  
  backend. We don't know each backend support what kind of options
  
  from the help message.
  Third, DNN backends duplicate DNN_BACKEND_COMMON_OPTIONS.
  Last but not the least, pass backend options via AV_OPT_TYPE_STRING
  
  makes it hard to pass AV_OPT_TYPE_BINARY to backend, if not impossible.
  This patch puts backend common options and each backend options inside
  
  DnnContext to reduce code duplication, make options user friendly, and
  
  easy to extend for future usecase.
  For example,
  ./ffmpeg -h filter=dnn_processing
  dnn_processing AVOptions :
  
     dnn_backend       <int>        ..FV....... DNN backend (from INT_MIN to INT_MAX) (default tensorflow)
  
       tensorflow      1            ..FV....... tensorflow backend flag
  
       openvino        2            ..FV....... openvino backend flag
  
       torch           3            ..FV....... torch backend flag
  dnn_base AVOptions :
  
     model             <string>     ..F........ path to model file
  
     input             <string>     ..F........ input name of the model
  
     output            <string>     ..F........ output name of the model
  
     backend_configs   <string>     ..F.......P backend configs (deprecated)
  
     options           <string>     ..F.......P backend configs (deprecated)
  
     nireq             <int>        ..F........ number of request (from 0 to INT_MAX) (default 0)
  
     async             <boolean>    ..F........ use DNN async inference (default true)
  
     device            <string>     ..F........ device to run model
  dnn_tensorflow AVOptions :
  
     sess_config       <string>     ..F........ config for SessionOptions
  dnn_openvino AVOptions :
  
     batch_size        <int>        ..F........ batch size per request (from 1 to 1000) (default 1)
  
     input_resizable   <boolean>    ..F........ can input be resizable or not (default false)
  
     layout            <int>        ..F........ input layout of model (from 0 to 2) (default none)
  
       none            0            ..F........ none
  
       nchw            1            ..F........ nchw
  
       nhwc            2            ..F........ nhwc
  
     scale             <float>      ..F........ Add scale preprocess operation. Divide each element of input by specified value. (from INT_MIN to INT_MAX) (default 0)
  
     mean              <float>      ..F........ Add mean preprocess operation. Subtract specified value from each element of input. (from INT_MIN to INT_MAX) (default 0)
  dnn_th AVOptions :
  
     optimize          <int>        ..F........ turn on graph executor optimization (from 0 to 1) (default 0)
  Signed-off-by : Zhao Zhili <zhilizhao@tencent.com>
  
  Reviewed-by : Wenbin Chen <wenbin.chen@intel.com>
  
  Reviewed-by : Guo Yejun <yejun.guo@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_common.h
  • [DH] libavfilter/dnn/dnn_backend_openvino.c
  • [DH] libavfilter/dnn/dnn_backend_tf.c
  • [DH] libavfilter/dnn/dnn_backend_torch.cpp
  • [DH] libavfilter/dnn/dnn_interface.c
  • [DH] libavfilter/dnn_filter_common.c
  • [DH] libavfilter/dnn_filter_common.h
  • [DH] libavfilter/dnn_interface.h
  • [DH] libavfilter/vf_derain.c
  • [DH] libavfilter/vf_dnn_classify.c
  • [DH] libavfilter/vf_dnn_detect.c
  • [DH] libavfilter/vf_dnn_processing.c
  • [DH] libavfilter/vf_sr.c

• Update Makefile.lite build system.

  23 juin 2014, par Evan Ramos

  Update Makefile.lite build system.
  Signed-off-by : Erik de Castro Lopo <erikd@mega-nerd.com>
  

  • [DH] Makefile.deps
  • [DH] Makefile.lite
  • [DH] build/compile.mk
  • [DH] build/config.mk
  • [DH] build/exe.mk
  • [DH] build/lib.mk
  • [DH] examples/c/decode/file/Makefile.lite
  • [DH] examples/c/encode/file/Makefile.lite
  • [DH] examples/cpp/decode/file/Makefile.lite
  • [DH] examples/cpp/encode/file/Makefile.lite
  • [DH] src/Makefile.lite
  • [DH] src/flac/Makefile.lite
  • [DH] src/flac/Makefile.lite.iffscan
  • [DH] src/libFLAC++/Makefile.lite
  • [DH] src/libFLAC/Makefile.lite
  • [DH] src/metaflac/Makefile.lite
  • [DH] src/plugin_xmms/Makefile.lite
  • [DH] src/share/Makefile.lite
  • [DH] src/share/getopt/Makefile.lite
  • [DH] src/share/grabbag/Makefile.lite
  • [DH] src/share/replaygain_analysis/Makefile.lite
  • [DH] src/share/replaygain_synthesis/Makefile.lite
  • [DH] src/share/utf8/Makefile.lite
  • [DH] src/share/win_utf8_io/Makefile.lite
  • [DH] src/test_grabbag/cuesheet/Makefile.lite
  • [DH] src/test_grabbag/picture/Makefile.lite
  • [DH] src/test_libFLAC++/Makefile.lite
  • [DH] src/test_libFLAC/Makefile.lite
  • [DH] src/test_libs_common/Makefile.lite
  • [DH] src/test_seeking/Makefile.lite
  • [DH] src/test_streams/Makefile.lite
  • [DH] src/utils/flacdiff/Makefile.lite
  • [DH] src/utils/flactimer/Makefile.lite

• using ffmpeg in Azure function to cut files using c#

  22 janvier 2019, par abhishekmoondra1989

  I have written an Azure function in C# which will cut a big mp4 files into some small duration. I have copied everything that is required (ffmpeg executeable, video file) in home directory via KUDU console. But when I run the the function it runs for more than 5 minutes and it doesn’t give any files in the home directory.

  Function :

  using System;
  
  using System.Diagnostics;
  
  
  
  public static void Run(string input, TraceWriter log)
  
  {
  
      log.Info("Executing");
  
      using (var process = new Process())
  
      {
  
          process.StartInfo.FileName = @"D:\home\ffmpeg.exe";
  
          process.StartInfo.Arguments = @"-i D:\home\AmnestyInternational.mp4 -ss 00:00:03 -t 00:00:08 -async 1 D:\home\cut.mp4";
  
          process.StartInfo.UseShellExecute = false;
  
          process.StartInfo.RedirectStandardOutput = true;
  
          process.StartInfo.RedirectStandardError = true;
  
          log.Info(Directory.GetCurrentDirectory());
  
          log.Info("Cutting starts:"+DateTime.Now.ToString("h:mm:ss tt"));
  
          process.Start();
  
          string output = process.StandardOutput.ReadToEnd();
  
          process.WaitForExit();
  
          log.Info("Cutting ends :"+DateTime.Now.ToString("h:mm:ss tt"));
  
          log.Info(output);
  
      }
  
  }

  Output seen on Azure function Console :

  2017-03-24T11:06:00.705 Function started (Id=df082f54-719a-415f-b7f1-b10548a213be)
  
  2017-03-24T11:06:00.721 Executing
  
  2017-03-24T11:06:00.721 D:\Windows\system32
  
  2017-03-24T11:06:00.721 Cutting start :11:06:00 AM
  
  2017-03-24T11:07:14  No new trace in the past 1 min(s).
  
  2017-03-24T11:08:14  No new trace in the past 2 min(s).
  
  2017-03-24T11:09:14  No new trace in the past 3 min(s).
  
  2017-03-24T11:10:14  No new trace in the past 4 min(s).
  
  2017-03-24T11:11:00.758 Microsoft.Azure.WebJobs.Host: Timeout value of 00:05:00 was exceeded by function: Functions.ManualTriggerCSharp1.

  When I try to execute this same command on KUDU console or my own PC it only take 1.5 mins and I get a file of the desired duration

  Could anyone please help me with this ? What I might be missing ?