/*
* 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;
DNNData 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;
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;
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] = CLIP((int)((((float *)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,
};