GCC Code Coverage Report
Directory: ../../../ffmpeg/ Exec Total Coverage
File: src/libavfilter/vf_derain.c Lines: 0 44 0.0 %
Date: 2020-09-25 14:59:26 Branches: 0 14 0.0 %

Line Branch Exec Source
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/*
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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 "libavformat/avio.h"
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#include "libavutil/opt.h"
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#include "avfilter.h"
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#include "dnn_interface.h"
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#include "formats.h"
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#include "internal.h"
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typedef struct DRContext {
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    const AVClass *class;
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    int                filter_type;
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    char              *model_filename;
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    DNNBackendType     backend_type;
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    DNNModule         *dnn_module;
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    DNNModel          *model;
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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, "type" },
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    { "derain",      "derain filter flag",          0,                      AV_OPT_TYPE_CONST,  { .i64 = 0 },    0, 0, FLAGS, "type" },
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    { "dehaze",      "dehaze filter flag",          0,                      AV_OPT_TYPE_CONST,  { .i64 = 1 },    0, 0, FLAGS, "type" },
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    { "dnn_backend", "DNN backend",                 OFFSET(backend_type),   AV_OPT_TYPE_INT,    { .i64 = 0 },    0, 1, FLAGS, "backend" },
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    { "native",      "native backend flag",         0,                      AV_OPT_TYPE_CONST,  { .i64 = 0 },    0, 0, FLAGS, "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, "backend" },
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#endif
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    { "model",       "path to model file",          OFFSET(model_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 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 query_formats(AVFilterContext *ctx)
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{
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    AVFilterFormats *formats;
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    const enum AVPixelFormat pixel_fmts[] = {
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        AV_PIX_FMT_RGB24,
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        AV_PIX_FMT_NONE
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    };
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    formats = ff_make_format_list(pixel_fmts);
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    return ff_set_common_formats(ctx, formats);
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}
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static int filter_frame(AVFilterLink *inlink, AVFrame *in)
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{
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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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    DNNReturnType dnn_result;
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    const char *model_output_name = "y";
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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 = (dr_context->dnn_module->execute_model)(dr_context->model, "x", in, &model_output_name, 1, out);
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    if (dnn_result != DNN_SUCCESS){
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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 AVERROR(EIO);
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    }
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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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    dr_context->dnn_module = ff_get_dnn_module(dr_context->backend_type);
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    if (!dr_context->dnn_module) {
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        av_log(ctx, AV_LOG_ERROR, "could not create DNN module for requested backend\n");
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        return AVERROR(ENOMEM);
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    }
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    if (!dr_context->model_filename) {
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        av_log(ctx, AV_LOG_ERROR, "model file for network is not specified\n");
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        return AVERROR(EINVAL);
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    }
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    if (!dr_context->dnn_module->load_model) {
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        av_log(ctx, AV_LOG_ERROR, "load_model for network is not specified\n");
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        return AVERROR(EINVAL);
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    }
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    dr_context->model = (dr_context->dnn_module->load_model)(dr_context->model_filename, NULL, NULL);
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    if (!dr_context->model) {
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        av_log(ctx, AV_LOG_ERROR, "could not load DNN model\n");
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        return AVERROR(EINVAL);
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    }
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    return 0;
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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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    if (dr_context->dnn_module) {
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        (dr_context->dnn_module->free_model)(&dr_context->model);
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        av_freep(&dr_context->dnn_module);
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    }
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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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    { NULL }
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};
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static const AVFilterPad derain_outputs[] = {
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    {
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        .name = "default",
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        .type = AVMEDIA_TYPE_VIDEO,
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    },
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    { NULL }
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};
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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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    .query_formats = query_formats,
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    .inputs        = derain_inputs,
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    .outputs       = derain_outputs,
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    .priv_class    = &derain_class,
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    .flags         = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
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};