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author | Xuewei Meng <xwmeng96@gmail.com> | 2019-07-27 10:59:26 +0800 |
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committer | Steven Liu <lq@chinaffmpeg.org> | 2019-08-22 15:25:47 +0800 |
commit | c87237d10511a28a3cfb7bb88ed2af1907dc8f66 (patch) | |
tree | 5b3f8dd0f30648bd330e5798dafd7cc181bb4821 | |
parent | aeae6283a9d5c84c1752bd270838ad738fff3d45 (diff) | |
download | ffmpeg-c87237d10511a28a3cfb7bb88ed2af1907dc8f66.tar.gz |
libavfilter: Update derain filter doc.
Add the usage of tensorflow model in derain filter. Training scripts
as well as scripts for tf/native model generation are provided in the
repository at https://github.com/XueweiMeng/derain_filter.git.
Reviewed-by: Steven Liu <lq@onvideo.cn>
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
-rw-r--r-- | doc/filters.texi | 16 |
1 files changed, 12 insertions, 4 deletions
diff --git a/doc/filters.texi b/doc/filters.texi index cf963413fc..323c02970e 100644 --- a/doc/filters.texi +++ b/doc/filters.texi @@ -8440,9 +8440,12 @@ Recurrent Squeeze-and-Excitation Context Aggregation Net (RESCAN). See @url{http://openaccess.thecvf.com/content_ECCV_2018/papers/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.pdf}. @end itemize -Training scripts as well as scripts for model generation are provided in +Training as well as model generation scripts are provided in the repository at @url{https://github.com/XueweiMeng/derain_filter.git}. +Native model files (.model) can be generated from TensorFlow model +files (.pb) by using tools/python/convert.py + The filter accepts the following options: @table @option @@ -8453,14 +8456,19 @@ the following values: @table @samp @item native Native implementation of DNN loading and execution. + +@item tensorflow +TensorFlow backend. To enable this backend you +need to install the TensorFlow for C library (see +@url{https://www.tensorflow.org/install/install_c}) and configure FFmpeg with +@code{--enable-libtensorflow} @end table Default value is @samp{native}. @item model Set path to model file specifying network architecture and its parameters. -Note that different backends use different file formats. TensorFlow backend -can load files for both formats, while native backend can load files for only -its format. +Note that different backends use different file formats. TensorFlow and native +backend can load files for only its format. @end table @section deshake |