mirror of
https://github.com/FFmpeg/FFmpeg.git
synced 2024-12-02 03:06:28 +02:00
59da9dcd7e
Signed-off-by: Steven Liu <lq@chinaffmpeg.org>
217 lines
7.2 KiB
C
217 lines
7.2 KiB
C
/*
|
|
* Copyright (c) 2019 Xuewei Meng
|
|
*
|
|
* 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
|
|
* Filter implementing image derain filter using deep convolutional networks.
|
|
* http://openaccess.thecvf.com/content_ECCV_2018/html/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.html
|
|
*/
|
|
|
|
#include "libavformat/avio.h"
|
|
#include "libavutil/opt.h"
|
|
#include "avfilter.h"
|
|
#include "dnn_interface.h"
|
|
#include "formats.h"
|
|
#include "internal.h"
|
|
|
|
typedef struct DRContext {
|
|
const AVClass *class;
|
|
|
|
int filter_type;
|
|
char *model_filename;
|
|
DNNBackendType backend_type;
|
|
DNNModule *dnn_module;
|
|
DNNModel *model;
|
|
DNNInputData input;
|
|
DNNData output;
|
|
} DRContext;
|
|
|
|
#define CLIP(x, min, max) (x < min ? min : (x > max ? max : x))
|
|
#define OFFSET(x) offsetof(DRContext, x)
|
|
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
|
|
static const AVOption derain_options[] = {
|
|
{ "filter_type", "filter type(derain/dehaze)", OFFSET(filter_type), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, 1, FLAGS, "type" },
|
|
{ "derain", "derain filter flag", 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, "type" },
|
|
{ "dehaze", "dehaze filter flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "type" },
|
|
{ "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 },
|
|
{ NULL }
|
|
};
|
|
|
|
AVFILTER_DEFINE_CLASS(derain);
|
|
|
|
static int query_formats(AVFilterContext *ctx)
|
|
{
|
|
AVFilterFormats *formats;
|
|
const enum AVPixelFormat pixel_fmts[] = {
|
|
AV_PIX_FMT_RGB24,
|
|
AV_PIX_FMT_NONE
|
|
};
|
|
|
|
formats = ff_make_format_list(pixel_fmts);
|
|
|
|
return ff_set_common_formats(ctx, formats);
|
|
}
|
|
|
|
static int config_inputs(AVFilterLink *inlink)
|
|
{
|
|
AVFilterContext *ctx = inlink->dst;
|
|
DRContext *dr_context = ctx->priv;
|
|
const char *model_output_name = "y";
|
|
DNNReturnType result;
|
|
|
|
dr_context->input.width = inlink->w;
|
|
dr_context->input.height = inlink->h;
|
|
dr_context->input.channels = 3;
|
|
|
|
result = (dr_context->model->set_input_output)(dr_context->model->model, &dr_context->input, "x", &model_output_name, 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 filter_frame(AVFilterLink *inlink, AVFrame *in)
|
|
{
|
|
AVFilterContext *ctx = inlink->dst;
|
|
AVFilterLink *outlink = ctx->outputs[0];
|
|
DRContext *dr_context = ctx->priv;
|
|
DNNReturnType dnn_result;
|
|
int pad_size;
|
|
|
|
AVFrame *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");
|
|
av_frame_free(&in);
|
|
return AVERROR(ENOMEM);
|
|
}
|
|
|
|
av_frame_copy_props(out, in);
|
|
|
|
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;
|
|
((float *)dr_context->input.data)[t] = in->data[0][k] / 255.0;
|
|
}
|
|
}
|
|
|
|
dnn_result = (dr_context->dnn_module->execute_model)(dr_context->model, &dr_context->output, 1);
|
|
if (dnn_result != DNN_SUCCESS){
|
|
av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
|
|
return AVERROR(EIO);
|
|
}
|
|
|
|
out->height = dr_context->output.height;
|
|
out->width = dr_context->output.width;
|
|
outlink->h = dr_context->output.height;
|
|
outlink->w = dr_context->output.width;
|
|
pad_size = (in->height - out->height) >> 1;
|
|
|
|
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;
|
|
|
|
int t_in = (i + pad_size) * in->width * 3 + j + pad_size * 3;
|
|
out->data[0][k] = CLIP((int)((((float *)dr_context->input.data)[t_in] - dr_context->output.data[t]) * 255), 0, 255);
|
|
}
|
|
}
|
|
|
|
av_frame_free(&in);
|
|
|
|
return ff_filter_frame(outlink, out);
|
|
}
|
|
|
|
static av_cold int init(AVFilterContext *ctx)
|
|
{
|
|
DRContext *dr_context = ctx->priv;
|
|
|
|
dr_context->input.dt = DNN_FLOAT;
|
|
dr_context->dnn_module = ff_get_dnn_module(dr_context->backend_type);
|
|
if (!dr_context->dnn_module) {
|
|
av_log(ctx, AV_LOG_ERROR, "could not create DNN module for requested backend\n");
|
|
return AVERROR(ENOMEM);
|
|
}
|
|
if (!dr_context->model_filename) {
|
|
av_log(ctx, AV_LOG_ERROR, "model file for network is not specified\n");
|
|
return AVERROR(EINVAL);
|
|
}
|
|
if (!dr_context->dnn_module->load_model) {
|
|
av_log(ctx, AV_LOG_ERROR, "load_model for network is not specified\n");
|
|
return AVERROR(EINVAL);
|
|
}
|
|
|
|
dr_context->model = (dr_context->dnn_module->load_model)(dr_context->model_filename);
|
|
if (!dr_context->model) {
|
|
av_log(ctx, AV_LOG_ERROR, "could not load DNN model\n");
|
|
return AVERROR(EINVAL);
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static av_cold void uninit(AVFilterContext *ctx)
|
|
{
|
|
DRContext *dr_context = ctx->priv;
|
|
|
|
if (dr_context->dnn_module) {
|
|
(dr_context->dnn_module->free_model)(&dr_context->model);
|
|
av_freep(&dr_context->dnn_module);
|
|
}
|
|
}
|
|
|
|
static const AVFilterPad derain_inputs[] = {
|
|
{
|
|
.name = "default",
|
|
.type = AVMEDIA_TYPE_VIDEO,
|
|
.config_props = config_inputs,
|
|
.filter_frame = filter_frame,
|
|
},
|
|
{ NULL }
|
|
};
|
|
|
|
static const AVFilterPad derain_outputs[] = {
|
|
{
|
|
.name = "default",
|
|
.type = AVMEDIA_TYPE_VIDEO,
|
|
},
|
|
{ NULL }
|
|
};
|
|
|
|
AVFilter ff_vf_derain = {
|
|
.name = "derain",
|
|
.description = NULL_IF_CONFIG_SMALL("Apply derain filter to the input."),
|
|
.priv_size = sizeof(DRContext),
|
|
.init = init,
|
|
.uninit = uninit,
|
|
.query_formats = query_formats,
|
|
.inputs = derain_inputs,
|
|
.outputs = derain_outputs,
|
|
.priv_class = &derain_class,
|
|
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
|
|
};
|