On 10/28/19, Paul B Mahol <one...@gmail.com> wrote:
> On 10/28/19, Guo, Yejun <yejun....@intel.com> wrote:
>>
>>
>>> -----Original Message-----
>>> From: Paul B Mahol [mailto:one...@gmail.com]
>>> Sent: Monday, October 28, 2019 4:00 PM
>>> To: FFmpeg development discussions and patches <ffmpeg-devel@ffmpeg.org>
>>> Cc: Guo, Yejun <yejun....@intel.com>
>>> Subject: Re: [FFmpeg-devel] [PATCH V2 4/4] avfilter/vf_dnn_processing:
>>> add
>>> a
>>> generic filter for image proccessing with dnn networks
>>>
>>> On 10/21/19, Guo, Yejun <yejun....@intel.com> wrote:
>>> > This filter accepts all the dnn networks which do image processing.
>>> > Currently, frame with formats rgb24 and bgr24 are supported. Other
>>> > formats such as gray and YUV will be supported next. The dnn network
>>> > can accept data in float32 or uint8 format. And the dnn network can
>>> > change frame size.
>>> >
>>> > Let's take an example with the following python script. This script
>>> > halves the value of the first channel of the pixel.
>>> > import tensorflow as tf
>>> > import numpy as np
>>> > import scipy.misc
>>> > in_img = scipy.misc.imread('in.bmp')
>>> > in_img = in_img.astype(np.float32)/255.0
>>> > in_data = in_img[np.newaxis, :]
>>> > filter_data = np.array([0.5, 0, 0, 0, 1., 0, 0, 0,
>>> > 1.]).reshape(1,1,3,3).astype(np.float32)
>>> > filter = tf.Variable(filter_data)
>>> > x = tf.placeholder(tf.float32, shape=[1, None, None, 3],
>>> > name='dnn_in')
>>> > y = tf.nn.conv2d(x, filter, strides=[1, 1, 1, 1], padding='VALID',
>>> > name='dnn_out')
>>> > sess=tf.Session()
>>> > sess.run(tf.global_variables_initializer())
>>> > output = sess.run(y, feed_dict={x: in_data})
>>> > graph_def = tf.graph_util.convert_variables_to_constants(sess,
>>> > sess.graph_def, ['dnn_out'])
>>> > tf.train.write_graph(graph_def, '.', 'halve_first_channel.pb',
>>> > as_text=False)
>>> > output = output * 255.0
>>> > output = output.astype(np.uint8)
>>> > scipy.misc.imsave("out.bmp", np.squeeze(output))
>>>
>>> So this one executes python code without ever returning or using
>>> AVFrame*
>>> ?
>>> This is extremely limited usage.
>>
>> the purpose of this script is to demo how to setup and execute dnn models
>> with python+tensorflow.
>> The only relationship with ffmpeg is that the script prepares the model
>> file
>> halve_first_channel.pb.
>>
>> The next description shows how ffmpeg can execute the model in a filter.
>>
>> I'll try to update the commit log to avoid misleading words, thanks.
>>
>>>
>>> >
>>> > - generate halve_first_channel.pb with the above script
>>> > - generate halve_first_channel.model with tools/python/convert.py
>>> > - try with following commands
>>> > ./ffmpeg -i input.jpg -vf
>>> >
>>> dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_
>>> out:fmt=rgb24:dnn_backend=native
>>> > -y out.native.png
>>> > ./ffmpeg -i input.jpg -vf
>>> >
>>> dnn_processing=model=halve_first_channel.pb:input=dnn_in:output=dnn_out:f
>>> mt=rgb24:dnn_backend=tensorflow
>>> > -y out.tf.png
>>> >
>>> > Signed-off-by: Guo, Yejun <yejun....@intel.com>
>>> > ---
>>> > configure | 1 +
>>> > doc/filters.texi | 44 ++++++
>>> > libavfilter/Makefile | 1 +
>>> > libavfilter/allfilters.c | 1 +
>>> > libavfilter/vf_dnn_processing.c | 333
>>> > ++++++++++++++++++++++++++++++++++++++++
>>> > 5 files changed, 380 insertions(+)
>>> > create mode 100644 libavfilter/vf_dnn_processing.c
>>> >
>>> > diff --git a/configure b/configure
>>> > index 8413826..bf2bac9 100755
>>> > --- a/configure
>>> > +++ b/configure
>>> > @@ -3460,6 +3460,7 @@ derain_filter_select="dnn"
>>> > deshake_filter_select="pixelutils"
>>> > deshake_opencl_filter_deps="opencl"
>>> > dilation_opencl_filter_deps="opencl"
>>> > +dnn_processing_filter_select="dnn"
>>> > drawtext_filter_deps="libfreetype"
>>> > drawtext_filter_suggest="libfontconfig libfribidi"
>>> > elbg_filter_deps="avcodec"
>>> > diff --git a/doc/filters.texi b/doc/filters.texi
>>> > index bdc4136..c11a616 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.
>>> > diff --git a/libavfilter/Makefile b/libavfilter/Makefile
>>> > index 63d2fba..47a485a 100644
>>> > --- a/libavfilter/Makefile
>>> > +++ b/libavfilter/Makefile
>>> > @@ -224,6 +224,7 @@ OBJS-$(CONFIG_DILATION_OPENCL_FILTER)
>>> +=
>>> > vf_neighbor_opencl.o opencl.o \
>>> >
>>> opencl/neighbor.o
>>> > OBJS-$(CONFIG_DISPLACE_FILTER) += vf_displace.o
>>> framesync.o
>>> > OBJS-$(CONFIG_DOUBLEWEAVE_FILTER) += vf_weave.o
>>> > +OBJS-$(CONFIG_DNN_PROCESSING_FILTER) +=
>>> vf_dnn_processing.o
>>> > OBJS-$(CONFIG_DRAWBOX_FILTER) += vf_drawbox.o
>>> > OBJS-$(CONFIG_DRAWGRAPH_FILTER) += f_drawgraph.o
>>> > OBJS-$(CONFIG_DRAWGRID_FILTER) += vf_drawbox.o
>>> > diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c
>>> > index e4186f9..485409f 100644
>>> > --- a/libavfilter/allfilters.c
>>> > +++ b/libavfilter/allfilters.c
>>> > @@ -209,6 +209,7 @@ extern AVFilter ff_vf_detelecine;
>>> > extern AVFilter ff_vf_dilation;
>>> > extern AVFilter ff_vf_dilation_opencl;
>>> > extern AVFilter ff_vf_displace;
>>> > +extern AVFilter ff_vf_dnn_processing;
>>> > extern AVFilter ff_vf_doubleweave;
>>> > extern AVFilter ff_vf_drawbox;
>>> > extern AVFilter ff_vf_drawgraph;
>>> > diff --git a/libavfilter/vf_dnn_processing.c
>>> > b/libavfilter/vf_dnn_processing.c
>>> > new file mode 100644
>>> > index 0000000..de89af4
>>> > --- /dev/null
>>> > +++ b/libavfilter/vf_dnn_processing.c
>>> > @@ -0,0 +1,333 @@
>>> > +/*
>>> > + * Copyright (c) 2019 Guo Yejun
>>> > + *
>>> > + * This file is part of FFmpeg.
>>> > + *
>>> > + * FFmpeg is free software; you can redistribute it and/or
>>> > + * modify it under the terms of the GNU Lesser General Public
>>> > + * License as published by the Free Software Foundation; either
>>> > + * version 2.1 of the License, or (at your option) any later version.
>>> > + *
>>> > + * FFmpeg is distributed in the hope that it will be useful,
>>> > + * but WITHOUT ANY WARRANTY; without even the implied warranty of
>>> > + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
>>> GNU
>>> > + * Lesser General Public License for more details.
>>> > + *
>>> > + * You should have received a copy of the GNU Lesser General Public
>>> > + * License along with FFmpeg; if not, write to the Free Software
>>> > + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
>>> > 02110-1301
>>> > USA
>>> > + */
>>> > +
>>> > +/**
>>> > + * @file
>>> > + * implementing a generic image processing filter using deep learning
>>> > networks.
>>> > + */
>>> > +
>>> > +#include "libavformat/avio.h"
>>> > +#include "libavutil/opt.h"
>>> > +#include "libavutil/pixdesc.h"
>>> > +#include "libavutil/avassert.h"
>>> > +#include "avfilter.h"
>>> > +#include "dnn_interface.h"
>>> > +#include "formats.h"
>>> > +#include "internal.h"
>>> > +
>>> > +typedef struct DnnProcessingContext {
>>> > + const AVClass *class;
>>> > +
>>> > + char *model_filename;
>>> > + DNNBackendType backend_type;
>>> > + enum AVPixelFormat fmt;
>>>
>>> This should be int.
>>
>> could you please help to explain a bit more why 'enum AVPixelFormat'
>> should
>> be int.
>>
>> I searched 'AV_OPT_TYPE_PIXEL_FMT' in vf_* files and found 'enum
>> AVPixelFormat' is used in
>> vf_mergeplanes.c, vf_program_opencl.c and vf_tonemap_opencl.c.
>
> That is error, I gonna fix mergeplanes ASAP. Sometimes enum may use
> completely another type, Michael may know more.
> Besides options assumes ints and not enums.
Actually they take also enums, so it should be safe. So ignore this one. Sorry.
>
>>
>>>
>>> > + char *model_inputname;
>>> > + char *model_outputname;
>>> > +
>>> > + DNNModule *dnn_module;
>>> > + DNNModel *model;
>>> > +
>>> > + // input & output of the model at execution time
>>> > + DNNData input;
>>> > + DNNData output;
>>> > +} DnnProcessingContext;
>>> > +
>>> > +#define OFFSET(x) offsetof(DnnProcessingContext, x)
>>> > +#define FLAGS AV_OPT_FLAG_FILTERING_PARAM |
>>> AV_OPT_FLAG_VIDEO_PARAM
>>> > +static const AVOption dnn_processing_options[] = {
>>> > + { "dnn_backend", "DNN backend",
>>> OFFSET(backend_type),
>>> > AV_OPT_TYPE_INT, { .i64 = 0 }, 0, 1, FLAGS, "backend" },
>>> > + { "native", "native backend flag", 0,
>>> > AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, "backend" },
>>> > +#if (CONFIG_LIBTENSORFLOW == 1)
>>> > + { "tensorflow", "tensorflow backend flag", 0,
>>> > AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "backend" },
>>> > +#endif
>>> > + { "model", "path to model file",
>>> OFFSET(model_filename),
>>> > AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
>>> > + { "input", "input name of the model",
>>> OFFSET(model_inputname),
>>> > AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
>>> > + { "output", "output name of the model",
>>> > OFFSET(model_outputname), AV_OPT_TYPE_STRING, { .str = NULL }, 0,
>>> > 0,
>>> > FLAGS },
>>> > + { "fmt", "AVPixelFormat of the frame", OFFSET(fmt),
>>> > AV_OPT_TYPE_PIXEL_FMT, { .i64=AV_PIX_FMT_RGB24 },
>>> AV_PIX_FMT_NONE,
>>> > AV_PIX_FMT_NB - 1, FLAGS },
>>> > + { NULL }
>>> > +};
>>> > +
>>> > +AVFILTER_DEFINE_CLASS(dnn_processing);
>>> > +
>>> > +static av_cold int init(AVFilterContext *context)
>>> > +{
>>> > + DnnProcessingContext *ctx = context->priv;
>>> > + int supported = 0;
>>> > + // as the first step, only rgb24 and bgr24 are supported
>>> > + const enum AVPixelFormat supported_pixel_fmts[] = {
>>> > + AV_PIX_FMT_RGB24,
>>> > + AV_PIX_FMT_BGR24,
>>> > + };
>>> > + for (int i = 0; i < sizeof(supported_pixel_fmts) / sizeof(enum
>>> > AVPixelFormat); ++i) {
>>> > + if (supported_pixel_fmts[i] == ctx->fmt) {
>>> > + supported = 1;
>>> > + break;
>>> > + }
>>> > + }
>>> > + if (!supported) {
>>> > + av_log(context, AV_LOG_ERROR, "pixel fmt %s not supported
>>> yet\n",
>>> > +
>>> av_get_pix_fmt_name(ctx->fmt));
>>> > + return AVERROR(AVERROR_INVALIDDATA);
>>> > + }
>>> > +
>>> > + if (!ctx->model_filename) {
>>> > + av_log(ctx, AV_LOG_ERROR, "model file for network is not
>>> > specified\n");
>>> > + return AVERROR(EINVAL);
>>> > + }
>>> > + if (!ctx->model_inputname) {
>>> > + av_log(ctx, AV_LOG_ERROR, "intput name of the model network
>>> > is
>>> not
>>>
>>> Typo
>>
>> thanks, will fix.
>>
>>>
>>> > specified\n");
>>> > + return AVERROR(EINVAL);
>>> > + }
>>> > + if (!ctx->model_outputname) {
>>> > + av_log(ctx, AV_LOG_ERROR, "output name of the model network
>>> is not
>>> > specified\n");
>>> > + return AVERROR(EINVAL);
>>> > + }
>>> > +
>>> > + ctx->dnn_module = ff_get_dnn_module(ctx->backend_type);
>>> > + if (!ctx->dnn_module) {
>>> > + av_log(ctx, AV_LOG_ERROR, "could not create DNN module for
>>> > requested backend\n");
>>> > + return AVERROR(ENOMEM);
>>> > + }
>>> > + if (!ctx->dnn_module->load_model) {
>>> > + av_log(ctx, AV_LOG_ERROR, "load_model for network is not
>>> > specified\n");
>>> > + return AVERROR(EINVAL);
>>> > + }
>>> > +
>>> > + ctx->model = (ctx->dnn_module->load_model)(ctx->model_filename);
>>> > + if (!ctx->model) {
>>> > + av_log(ctx, AV_LOG_ERROR, "could not load DNN model\n");
>>> > + return AVERROR(EINVAL);
>>> > + }
>>> > +
>>> > + return 0;
>>> > +}
>>> > +
>>> > +static int query_formats(AVFilterContext *context)
>>> > +{
>>> > + AVFilterFormats *formats;
>>> > + DnnProcessingContext *ctx = context->priv;
>>> > + enum AVPixelFormat pixel_fmts[2];
>>> > + pixel_fmts[0] = ctx->fmt;
>>> > + pixel_fmts[1] = AV_PIX_FMT_NONE;
>>> > +
>>> > + formats = ff_make_format_list(pixel_fmts);
>>> > + return ff_set_common_formats(context, formats);
>>> > +}
>>> > +
>>> > +static int config_input(AVFilterLink *inlink)
>>> > +{
>>> > + AVFilterContext *context = inlink->dst;
>>> > + DnnProcessingContext *ctx = context->priv;
>>> > + DNNReturnType result;
>>> > + DNNData dnn_data;
>>> > +
>>> > + result = ctx->model->get_input(ctx->model->model, &dnn_data,
>>> > ctx->model_inputname);
>>> > + if (result != DNN_SUCCESS) {
>>> > + av_log(ctx, AV_LOG_ERROR, "could not get input from the
>>> model\n");
>>> > + return AVERROR(EIO);
>>> > + }
>>> > +
>>> > + // the design is to add explicit scale filter before this filter
>>> > + if (dnn_data.height != -1 && dnn_data.height != inlink->h) {
>>> > + av_log(ctx, AV_LOG_ERROR, "the model requires frame height %d
>>> but
>>> > got %d\n",
>>> > + dnn_data.height, inlink->h);
>>> > + return AVERROR(EIO);
>>> > + }
>>> > + if (dnn_data.width != -1 && dnn_data.width != inlink->w) {
>>> > + av_log(ctx, AV_LOG_ERROR, "the model requires frame width %d
>>> but
>>> > got %d\n",
>>> > + dnn_data.width, inlink->w);
>>> > + return AVERROR(EIO);
>>> > + }
>>> > +
>>> > + if (dnn_data.channels != 3) {
>>> > + av_log(ctx, AV_LOG_ERROR, "the model requires input
>>> channels %d\n",
>>> > + dnn_data.channels);
>>> > + return AVERROR(EIO);
>>> > + }
>>> > + if (dnn_data.dt != DNN_FLOAT && dnn_data.dt != DNN_UINT8) {
>>> > + av_log(ctx, AV_LOG_ERROR, "only support dnn models with input
>>> data
>>> > type as float32 and uint8.\n");
>>> > + return AVERROR(EIO);
>>> > + }
>>> > +
>>> > + ctx->input.width = inlink->w;
>>> > + ctx->input.height = inlink->h;
>>> > + ctx->input.channels = dnn_data.channels;
>>> > + ctx->input.dt = dnn_data.dt;
>>> > +
>>> > + result = (ctx->model->set_input_output)(ctx->model->model,
>>> > + &ctx->input,
>>> ctx->model_inputname,
>>> > + (const char
>>> > **)&ctx->model_outputname, 1);
>>> > + if (result != DNN_SUCCESS) {
>>> > + av_log(ctx, AV_LOG_ERROR, "could not set input and output for
>>> the
>>> > model\n");
>>> > + return AVERROR(EIO);
>>> > + }
>>> > +
>>> > + return 0;
>>> > +}
>>> > +
>>> > +static int config_output(AVFilterLink *outlink)
>>> > +{
>>> > + AVFilterContext *context = outlink->src;
>>> > + DnnProcessingContext *ctx = context->priv;
>>> > + DNNReturnType result;
>>> > +
>>> > + // have a try run in case that the dnn model resize the frame
>>> > + result = (ctx->dnn_module->execute_model)(ctx->model,
>>> > &ctx->output,
>>> 1);
>>> > + if (result != DNN_SUCCESS){
>>> > + av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
>>> > + return AVERROR(EIO);
>>> > + }
>>> > +
>>> > + outlink->w = ctx->output.width;
>>> > + outlink->h = ctx->output.height;
>>> > +
>>> > + return 0;
>>> > +}
>>> > +
>>> > +static int copy_from_frame_to_dnn(DNNData *dnn_data, const AVFrame
>>> *in)
>>> > +{
>>> > + // extend this function to support more formats
>>> > + av_assert0(in->format == AV_PIX_FMT_RGB24 || in->format ==
>>> > AV_PIX_FMT_RGB24);
>>> > +
>>> > + if (dnn_data->dt == DNN_FLOAT) {
>>> > + float *dnn_input = dnn_data->data;
>>> > + for (int i = 0; i < in->height; i++) {
>>> > + for(int j = 0; j < in->width * 3; j++) {
>>> > + int k = i * in->linesize[0] + j;
>>> > + int t = i * in->width * 3 + j;
>>> > + dnn_input[t] = in->data[0][k] / 255.0f;
>>> > + }
>>> > + }
>>> > + } else {
>>> > + uint8_t *dnn_input = dnn_data->data;
>>> > + av_assert0(dnn_data->dt == DNN_UINT8);
>>> > + for (int i = 0; i < in->height; i++) {
>>> > + for(int j = 0; j < in->width * 3; j++) {
>>> > + int k = i * in->linesize[0] + j;
>>> > + int t = i * in->width * 3 + j;
>>> > + dnn_input[t] = in->data[0][k];
>>> > + }
>>> > + }
>>> > + }
>>> > +
>>> > + return 0;
>>> > +}
>>> > +
>>> > +static int copy_from_dnn_to_frame(AVFrame *out, const DNNData
>>> *dnn_data)
>>> > +{
>>> > + // extend this function to support more formats
>>> > + av_assert0(out->format == AV_PIX_FMT_RGB24 || out->format ==
>>> > AV_PIX_FMT_RGB24);
>>> > +
>>> > + if (dnn_data->dt == DNN_FLOAT) {
>>> > + float *dnn_output = dnn_data->data;
>>> > + for (int i = 0; i < out->height; i++) {
>>> > + for(int j = 0; j < out->width * 3; j++) {
>>> > + int k = i * out->linesize[0] + j;
>>> > + int t = i * out->width * 3 + j;
>>> > + out->data[0][k] = av_clip((int)(dnn_output[t] *
>>> > 255.0f), 0,
>>> > 255);
>>> > + }
>>> > + }
>>> > + } else {
>>> > + uint8_t *dnn_output = dnn_data->data;
>>> > + av_assert0(dnn_data->dt == DNN_UINT8);
>>> > + for (int i = 0; i < out->height; i++) {
>>> > + for(int j = 0; j < out->width * 3; j++) {
>>> > + int k = i * out->linesize[0] + j;
>>> > + int t = i * out->width * 3 + j;
>>> > + out->data[0][k] = dnn_output[t];
>>> > + }
>>> > + }
>>> > + }
>>> > +
>>> > + return 0;
>>> > +}
>>> > +
>>> > +static int filter_frame(AVFilterLink *inlink, AVFrame *in)
>>> > +{
>>> > + AVFilterContext *context = inlink->dst;
>>> > + AVFilterLink *outlink = context->outputs[0];
>>> > + DnnProcessingContext *ctx = context->priv;
>>> > + DNNReturnType dnn_result;
>>> > + AVFrame *out;
>>> > +
>>> > + copy_from_frame_to_dnn(&ctx->input, in);
>>> > +
>>> > + dnn_result = (ctx->dnn_module->execute_model)(ctx->model,
>>> &ctx->output,
>>> > 1);
>>> > + if (dnn_result != DNN_SUCCESS){
>>> > + av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
>>> > + av_frame_free(&in);
>>> > + return AVERROR(EIO);
>>> > + }
>>> > + av_assert0(ctx->output.channels == 3);
>>> > +
>>> > + out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
>>> > + if (!out) {
>>> > + av_log(ctx, AV_LOG_ERROR, "could not allocate memory for
>>> output
>>> > frame\n");
>>>
>>> This log message should be removed, as it is not useful at all.
>>
>> thanks, will remove
>>
>>>
>>> > + av_frame_free(&in);
>>> > + return AVERROR(ENOMEM);
>>> > + }
>>> > +
>>> > + av_frame_copy_props(out, in);
>>> > + copy_from_dnn_to_frame(out, &ctx->output);
>>> > + av_frame_free(&in);
>>> > + return ff_filter_frame(outlink, out);
>>> > +}
>>> > +
>>> > +static av_cold void uninit(AVFilterContext *ctx)
>>> > +{
>>> > + DnnProcessingContext *context = ctx->priv;
>>> > +
>>> > + if (context->dnn_module)
>>> > + (context->dnn_module->free_model)(&context->model);
>>> > +
>>> > + av_freep(&context->dnn_module);
>>> > +}
>>> > +
>>> > +static const AVFilterPad dnn_processing_inputs[] = {
>>> > + {
>>> > + .name = "default",
>>> > + .type = AVMEDIA_TYPE_VIDEO,
>>> > + .config_props = config_input,
>>> > + .filter_frame = filter_frame,
>>> > + },
>>> > + { NULL }
>>> > +};
>>> > +
>>> > +static const AVFilterPad dnn_processing_outputs[] = {
>>> > + {
>>> > + .name = "default",
>>> > + .type = AVMEDIA_TYPE_VIDEO,
>>> > + .config_props = config_output,
>>> > + },
>>> > + { NULL }
>>> > +};
>>> > +
>>> > +AVFilter ff_vf_dnn_processing = {
>>> > + .name = "dnn_processing",
>>> > + .description = NULL_IF_CONFIG_SMALL("Apply DNN processing
>>> filter to
>>> > the input."),
>>> > + .priv_size = sizeof(DnnProcessingContext),
>>> > + .init = init,
>>> > + .uninit = uninit,
>>> > + .query_formats = query_formats,
>>> > + .inputs = dnn_processing_inputs,
>>> > + .outputs = dnn_processing_outputs,
>>> > + .priv_class = &dnn_processing_class,
>>> > + .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
>>>
>>> If filter changes w/h, this can not be supported.
>>
>> got it, will remove this line, thank you.
>>
>>>
>>> > +};
>>> > --
>>> > 2.7.4
>>> >
>>> > _______________________________________________
>>> > ffmpeg-devel mailing list
>>> > ffmpeg-devel@ffmpeg.org
>>> > https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
>>> >
>>> > To unsubscribe, visit link above, or email
>>> > ffmpeg-devel-requ...@ffmpeg.org with subject "unsubscribe".
>>
>
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