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/*
* 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 "libavutil/opt.h"
#include "avfilter.h"
#include "dnn_filter_common.h"
#include "internal.h"
#include "video.h"
typedef struct DRContext {
const AVClass *class;
DnnContext dnnctx;
int filter_type;
} DRContext;
#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(dnnctx.backend_type), AV_OPT_TYPE_INT, { .i64 = 1 }, 0, 1, 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(dnnctx.model_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
{ "input", "input name of the model", OFFSET(dnnctx.model_inputname), AV_OPT_TYPE_STRING, { .str = "x" }, 0, 0, FLAGS },
{ "output", "output name of the model", OFFSET(dnnctx.model_outputnames_string), AV_OPT_TYPE_STRING, { .str = "y" }, 0, 0, FLAGS },
{ NULL }
};
AVFILTER_DEFINE_CLASS(derain);
static int filter_frame(AVFilterLink *inlink, AVFrame *in)
{
DNNAsyncStatusType async_state = 0;
AVFilterContext *ctx = inlink->dst;
AVFilterLink *outlink = ctx->outputs[0];
DRContext *dr_context = ctx->priv;
int dnn_result;
AVFrame *out;
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);
dnn_result = ff_dnn_execute_model(&dr_context->dnnctx, in, out);
if (dnn_result != 0){
av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
av_frame_free(&in);
return dnn_result;
}
do {
async_state = ff_dnn_get_result(&dr_context->dnnctx, &in, &out);
} while (async_state == DAST_NOT_READY);
if (async_state != DAST_SUCCESS)
return AVERROR(EINVAL);
av_frame_free(&in);
return ff_filter_frame(outlink, out);
}
static av_cold int init(AVFilterContext *ctx)
{
DRContext *dr_context = ctx->priv;
return ff_dnn_init(&dr_context->dnnctx, DFT_PROCESS_FRAME, ctx);
}
static av_cold void uninit(AVFilterContext *ctx)
{
DRContext *dr_context = ctx->priv;
ff_dnn_uninit(&dr_context->dnnctx);
}
static const AVFilterPad derain_inputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.filter_frame = filter_frame,
},
};
const 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,
FILTER_INPUTS(derain_inputs),
FILTER_OUTPUTS(ff_video_default_filterpad),
FILTER_SINGLE_PIXFMT(AV_PIX_FMT_RGB24),
.priv_class = &derain_class,
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
};
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