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diff --git a/doc/filters.texi b/doc/filters.texi index 6d893d8b87..6800124574 100644 --- a/doc/filters.texi +++ b/doc/filters.texi @@ -8928,6 +8928,50 @@ ffmpeg -i INPUT -f lavfi -i nullsrc=hd720,geq='r=128+80*(sin(sqrt((X-W/2)*(X-W/2 @end example @end itemize +@section dnn_processing + +Do image processing with deep neural networks. Currently only AVFrame with RGB24 +and BGR24 are supported, more formats will be added later. + +The filter accepts the following options: + +@table @option +@item dnn_backend +Specify which DNN backend to use for model loading and execution. This option accepts +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 and native +backend can load files for only its format. + +Native model file (.model) can be generated from TensorFlow model file (.pb) by using tools/python/convert.py + +@item input +Set the input name of the dnn network. + +@item output +Set the output name of the dnn network. + +@item fmt +Set the pixel format for the Frame. Allowed values are @code{AV_PIX_FMT_RGB24}, and @code{AV_PIX_FMT_BGR24}. +Default value is @code{AV_PIX_FMT_RGB24}. + +@end table + @section drawbox Draw a colored box on the input image. |