Hi,
Em qui., 31 de out. de 2019 às 05:39, Guo, Yejun <yejun....@intel.com>
escreveu:
> 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.
>
> The following is a python script to halve the value of the first
> channel of the pixel. It demos how to setup and execute dnn model
> with python+tensorflow. It also generates .pb file which will be
> used by ffmpeg.
>
> 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))
>
> To do the same thing with ffmpeg:
> - 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:fmt=rgb24:dnn_backend=tensorflow
> -y out.tf.png
>
It would be great if you could transform the above steps in a fate test,
that way one can automatically ensure the filter is always working properly.
>
> 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 | 331
> ++++++++++++++++++++++++++++++++++++++++
> 5 files changed, 378 insertions(+)
> create mode 100644 libavfilter/vf_dnn_processing.c
>
> diff --git a/configure b/configure
> index 875b77f..4b3964d 100755
> --- a/configure
> +++ b/configure
> @@ -3463,6 +3463,7 @@ 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 9d387be..15771ab 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 2080eed..3eff398 100644
> --- a/libavfilter/Makefile
> +++ b/libavfilter/Makefile
> @@ -223,6 +223,7 @@ OBJS-$(CONFIG_DILATION_FILTER) +=
> vf_neighbor.o
> OBJS-$(CONFIG_DILATION_OPENCL_FILTER) += vf_neighbor_opencl.o
> opencl.o \
> opencl/neighbor.o
> OBJS-$(CONFIG_DISPLACE_FILTER) += vf_displace.o framesync.o
> +OBJS-$(CONFIG_DNN_PROCESSING_FILTER) += vf_dnn_processing.o
> OBJS-$(CONFIG_DOUBLEWEAVE_FILTER) += vf_weave.o
> OBJS-$(CONFIG_DRAWBOX_FILTER) += vf_drawbox.o
> OBJS-$(CONFIG_DRAWGRAPH_FILTER) += f_drawgraph.o
> diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c
> index cb60906..7c1e19e 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..87ad354
> --- /dev/null
> +++ b/libavfilter/vf_dnn_processing.c
> @@ -0,0 +1,331 @@
> +/*
> + * 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;
> + 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, "input name of the model network is not
> 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_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,
> +};
> --
> 2.7.4
>
rest LGTM.
BTW do you have already concrete use cases (or plans) for this filter?
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