> -----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.
>
> > + 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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