[FFmpeg-cvslog] libavfilter: Update derain filter doc.

[Xuewei Meng]

ffmpeg | branch: master | Xuewei Meng <xwmeng96 at gmail.com> | Sat Jul 27 10:59:26 2019 +0800| [c87237d10511a28a3cfb7bb88ed2af1907dc8f66] | committer: Steven Liu

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 at onvideo.cn>
Signed-off-by: Xuewei Meng <xwmeng96 at gmail.com>

> http://git.videolan.org/gitweb.cgi/ffmpeg.git/?a=commit;h=c87237d10511a28a3cfb7bb88ed2af1907dc8f66
---

 doc/filters.texi | 16 ++++++++++++----
 1 file 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.
+
+ at item tensorflow
+TensorFlow backend. To enable this backend you
+need to install the TensorFlow for C library (see
+ at url{https://www.tensorflow.org/install/install_c}) and configure FFmpeg with
+ at 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