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ffmpeg/libavfilter/vf_dnn_processing.c

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/*
* 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 "libavutil/imgutils.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;
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 },
{ NULL }
};
AVFILTER_DEFINE_CLASS(dnn_processing);
static av_cold int init(AVFilterContext *context)
{
DnnProcessingContext *ctx = context->priv;
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)
{
static const enum AVPixelFormat pix_fmts[] = {
AV_PIX_FMT_RGB24, AV_PIX_FMT_BGR24,
AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAYF32,
AV_PIX_FMT_NONE
};
AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
return ff_set_common_formats(context, fmts_list);
}
#define LOG_FORMAT_CHANNEL_MISMATCH() \
av_log(ctx, AV_LOG_ERROR, \
"the frame's format %s does not match " \
"the model input channel %d\n", \
av_get_pix_fmt_name(fmt), \
model_input->channels);
static int check_modelinput_inlink(const DNNData *model_input, const AVFilterLink *inlink)
{
AVFilterContext *ctx = inlink->dst;
enum AVPixelFormat fmt = inlink->format;
// the design is to add explicit scale filter before this filter
if (model_input->height != -1 && model_input->height != inlink->h) {
av_log(ctx, AV_LOG_ERROR, "the model requires frame height %d but got %d\n",
model_input->height, inlink->h);
return AVERROR(EIO);
}
if (model_input->width != -1 && model_input->width != inlink->w) {
av_log(ctx, AV_LOG_ERROR, "the model requires frame width %d but got %d\n",
model_input->width, inlink->w);
return AVERROR(EIO);
}
switch (fmt) {
case AV_PIX_FMT_RGB24:
case AV_PIX_FMT_BGR24:
if (model_input->channels != 3) {
LOG_FORMAT_CHANNEL_MISMATCH();
return AVERROR(EIO);
}
if (model_input->dt != DNN_FLOAT && model_input->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);
}
return 0;
case AV_PIX_FMT_GRAY8:
if (model_input->channels != 1) {
LOG_FORMAT_CHANNEL_MISMATCH();
return AVERROR(EIO);
}
if (model_input->dt != DNN_UINT8) {
av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type uint8.\n");
return AVERROR(EIO);
}
return 0;
case AV_PIX_FMT_GRAYF32:
if (model_input->channels != 1) {
LOG_FORMAT_CHANNEL_MISMATCH();
return AVERROR(EIO);
}
if (model_input->dt != DNN_FLOAT) {
av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type float32.\n");
return AVERROR(EIO);
}
return 0;
default:
av_log(ctx, AV_LOG_ERROR, "%s not supported.\n", av_get_pix_fmt_name(fmt));
return AVERROR(EIO);
}
return 0;
}
static int config_input(AVFilterLink *inlink)
{
AVFilterContext *context = inlink->dst;
DnnProcessingContext *ctx = context->priv;
DNNReturnType result;
DNNData model_input;
int check;
result = ctx->model->get_input(ctx->model->model, &model_input, ctx->model_inputname);
if (result != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "could not get input from the model\n");
return AVERROR(EIO);
}
check = check_modelinput_inlink(&model_input, inlink);
if (check != 0) {
return check;
}
ctx->input.width = inlink->w;
ctx->input.height = inlink->h;
ctx->input.channels = model_input.channels;
ctx->input.dt = model_input.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_input, const AVFrame *frame)
{
int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
switch (frame->format) {
case AV_PIX_FMT_RGB24:
case AV_PIX_FMT_BGR24:
if (dnn_input->dt == DNN_FLOAT) {
float *dnn_input_data = dnn_input->data;
for (int i = 0; i < frame->height; i++) {
for(int j = 0; j < frame->width * 3; j++) {
int k = i * frame->linesize[0] + j;
int t = i * frame->width * 3 + j;
dnn_input_data[t] = frame->data[0][k] / 255.0f;
}
}
} else {
av_assert0(dnn_input->dt == DNN_UINT8);
av_image_copy_plane(dnn_input->data, bytewidth,
frame->data[0], frame->linesize[0],
bytewidth, frame->height);
}
return 0;
case AV_PIX_FMT_GRAY8:
case AV_PIX_FMT_GRAYF32:
av_image_copy_plane(dnn_input->data, bytewidth,
frame->data[0], frame->linesize[0],
bytewidth, frame->height);
return 0;
default:
return AVERROR(EIO);
}
return 0;
}
static int copy_from_dnn_to_frame(AVFrame *frame, const DNNData *dnn_output)
{
int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
switch (frame->format) {
case AV_PIX_FMT_RGB24:
case AV_PIX_FMT_BGR24:
if (dnn_output->dt == DNN_FLOAT) {
float *dnn_output_data = dnn_output->data;
for (int i = 0; i < frame->height; i++) {
for(int j = 0; j < frame->width * 3; j++) {
int k = i * frame->linesize[0] + j;
int t = i * frame->width * 3 + j;
frame->data[0][k] = av_clip_uintp2((int)(dnn_output_data[t] * 255.0f), 8);
}
}
} else {
av_assert0(dnn_output->dt == DNN_UINT8);
av_image_copy_plane(frame->data[0], frame->linesize[0],
dnn_output->data, bytewidth,
bytewidth, frame->height);
}
return 0;
case AV_PIX_FMT_GRAY8:
// it is possible that data type of dnn output is float32,
// need to add support for such case when needed.
av_assert0(dnn_output->dt == DNN_UINT8);
av_image_copy_plane(frame->data[0], frame->linesize[0],
dnn_output->data, bytewidth,
bytewidth, frame->height);
return 0;
case AV_PIX_FMT_GRAYF32:
av_assert0(dnn_output->dt == DNN_FLOAT);
av_image_copy_plane(frame->data[0], frame->linesize[0],
dnn_output->data, bytewidth,
bytewidth, frame->height);
return 0;
default:
return AVERROR(EIO);
}
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);
}
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,
};
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