124 lines
4.5 KiB
C
124 lines
4.5 KiB
C
/*
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* Copyright (c) 2019 Xuewei Meng
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*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*/
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/**
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* @file
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* Filter implementing image derain filter using deep convolutional networks.
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* http://openaccess.thecvf.com/content_ECCV_2018/html/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.html
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*/
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#include "libavutil/opt.h"
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#include "avfilter.h"
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#include "dnn_filter_common.h"
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#include "internal.h"
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#include "video.h"
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typedef struct DRContext {
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const AVClass *class;
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DnnContext dnnctx;
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int filter_type;
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} DRContext;
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#define OFFSET(x) offsetof(DRContext, x)
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#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
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static const AVOption derain_options[] = {
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{ "filter_type", "filter type(derain/dehaze)", OFFSET(filter_type), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, 1, FLAGS, .unit = "type" },
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{ "derain", "derain filter flag", 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, .unit = "type" },
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{ "dehaze", "dehaze filter flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, .unit = "type" },
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{ "dnn_backend", "DNN backend", OFFSET(dnnctx.backend_type), AV_OPT_TYPE_INT, { .i64 = 1 }, 0, 1, FLAGS, .unit = "backend" },
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#if (CONFIG_LIBTENSORFLOW == 1)
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{ "tensorflow", "tensorflow backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, .unit = "backend" },
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#endif
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{ "model", "path to model file", OFFSET(dnnctx.model_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
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{ "input", "input name of the model", OFFSET(dnnctx.model_inputname), AV_OPT_TYPE_STRING, { .str = "x" }, 0, 0, FLAGS },
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{ "output", "output name of the model", OFFSET(dnnctx.model_outputnames_string), AV_OPT_TYPE_STRING, { .str = "y" }, 0, 0, FLAGS },
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{ NULL }
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};
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AVFILTER_DEFINE_CLASS(derain);
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static int filter_frame(AVFilterLink *inlink, AVFrame *in)
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{
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DNNAsyncStatusType async_state = 0;
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AVFilterContext *ctx = inlink->dst;
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AVFilterLink *outlink = ctx->outputs[0];
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DRContext *dr_context = ctx->priv;
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int dnn_result;
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AVFrame *out;
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out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
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if (!out) {
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av_log(ctx, AV_LOG_ERROR, "could not allocate memory for output frame\n");
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av_frame_free(&in);
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return AVERROR(ENOMEM);
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}
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av_frame_copy_props(out, in);
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dnn_result = ff_dnn_execute_model(&dr_context->dnnctx, in, out);
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if (dnn_result != 0){
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av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
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av_frame_free(&in);
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return dnn_result;
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}
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do {
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async_state = ff_dnn_get_result(&dr_context->dnnctx, &in, &out);
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} while (async_state == DAST_NOT_READY);
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if (async_state != DAST_SUCCESS)
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return AVERROR(EINVAL);
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av_frame_free(&in);
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return ff_filter_frame(outlink, out);
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}
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static av_cold int init(AVFilterContext *ctx)
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{
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DRContext *dr_context = ctx->priv;
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return ff_dnn_init(&dr_context->dnnctx, DFT_PROCESS_FRAME, ctx);
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}
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static av_cold void uninit(AVFilterContext *ctx)
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{
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DRContext *dr_context = ctx->priv;
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ff_dnn_uninit(&dr_context->dnnctx);
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}
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static const AVFilterPad derain_inputs[] = {
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{
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.name = "default",
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.type = AVMEDIA_TYPE_VIDEO,
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.filter_frame = filter_frame,
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},
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};
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const AVFilter ff_vf_derain = {
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.name = "derain",
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.description = NULL_IF_CONFIG_SMALL("Apply derain filter to the input."),
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.priv_size = sizeof(DRContext),
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.init = init,
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.uninit = uninit,
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FILTER_INPUTS(derain_inputs),
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FILTER_OUTPUTS(ff_video_default_filterpad),
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FILTER_SINGLE_PIXFMT(AV_PIX_FMT_RGB24),
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.priv_class = &derain_class,
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.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
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};
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