246 lines
7.9 KiB
C
246 lines
7.9 KiB
C
/*
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* Copyright (c) 2018 Sergey Lavrushkin
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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 super-resolution using deep convolutional networks.
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* https://arxiv.org/abs/1501.00092
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*/
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#include "avfilter.h"
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#include "formats.h"
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#include "internal.h"
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#include "libavutil/opt.h"
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#include "libavformat/avio.h"
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#include "dnn_interface.h"
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typedef struct SRCNNContext {
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const AVClass *class;
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char* model_filename;
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float* input_output_buf;
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DNNModule* dnn_module;
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DNNModel* model;
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DNNData input_output;
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} SRCNNContext;
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#define OFFSET(x) offsetof(SRCNNContext, x)
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#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
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static const AVOption srcnn_options[] = {
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{ "model_filename", "path to model file specifying network architecture and its parameters", 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(srcnn);
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static av_cold int init(AVFilterContext* context)
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{
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SRCNNContext* srcnn_context = context->priv;
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srcnn_context->dnn_module = ff_get_dnn_module(DNN_NATIVE);
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if (!srcnn_context->dnn_module){
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av_log(context, AV_LOG_ERROR, "could not create dnn module\n");
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return AVERROR(ENOMEM);
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}
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if (!srcnn_context->model_filename){
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av_log(context, AV_LOG_INFO, "model file for network was not specified, using default network for x2 upsampling\n");
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srcnn_context->model = (srcnn_context->dnn_module->load_default_model)(DNN_SRCNN);
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}
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else{
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srcnn_context->model = (srcnn_context->dnn_module->load_model)(srcnn_context->model_filename);
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}
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if (!srcnn_context->model){
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av_log(context, AV_LOG_ERROR, "could not load dnn model\n");
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return AVERROR(EIO);
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}
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return 0;
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}
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static int query_formats(AVFilterContext* context)
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{
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const enum AVPixelFormat pixel_formats[] = {AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P, AV_PIX_FMT_YUV444P,
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AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P, AV_PIX_FMT_GRAY8,
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AV_PIX_FMT_NONE};
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AVFilterFormats* formats_list;
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formats_list = ff_make_format_list(pixel_formats);
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if (!formats_list){
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av_log(context, AV_LOG_ERROR, "could not create formats list\n");
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return AVERROR(ENOMEM);
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}
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return ff_set_common_formats(context, formats_list);
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}
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static int config_props(AVFilterLink* inlink)
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{
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AVFilterContext* context = inlink->dst;
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SRCNNContext* srcnn_context = context->priv;
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DNNReturnType result;
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srcnn_context->input_output_buf = av_malloc(inlink->h * inlink->w * sizeof(float));
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if (!srcnn_context->input_output_buf){
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av_log(context, AV_LOG_ERROR, "could not allocate memory for input/output buffer\n");
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return AVERROR(ENOMEM);
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}
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srcnn_context->input_output.data = srcnn_context->input_output_buf;
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srcnn_context->input_output.width = inlink->w;
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srcnn_context->input_output.height = inlink->h;
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srcnn_context->input_output.channels = 1;
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result = (srcnn_context->model->set_input_output)(srcnn_context->model->model, &srcnn_context->input_output, &srcnn_context->input_output);
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if (result != DNN_SUCCESS){
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av_log(context, AV_LOG_ERROR, "could not set input and output for the model\n");
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return AVERROR(EIO);
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}
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else{
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return 0;
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}
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}
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typedef struct ThreadData{
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uint8_t* out;
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int out_linesize, height, width;
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} ThreadData;
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static int uint8_to_float(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
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{
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SRCNNContext* srcnn_context = context->priv;
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const ThreadData* td = arg;
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const int slice_start = (td->height * jobnr ) / nb_jobs;
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const int slice_end = (td->height * (jobnr + 1)) / nb_jobs;
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const uint8_t* src = td->out + slice_start * td->out_linesize;
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float* dst = srcnn_context->input_output_buf + slice_start * td->width;
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int y, x;
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for (y = slice_start; y < slice_end; ++y){
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for (x = 0; x < td->width; ++x){
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dst[x] = (float)src[x] / 255.0f;
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}
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src += td->out_linesize;
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dst += td->width;
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}
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return 0;
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}
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static int float_to_uint8(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
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{
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SRCNNContext* srcnn_context = context->priv;
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const ThreadData* td = arg;
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const int slice_start = (td->height * jobnr ) / nb_jobs;
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const int slice_end = (td->height * (jobnr + 1)) / nb_jobs;
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const float* src = srcnn_context->input_output_buf + slice_start * td->width;
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uint8_t* dst = td->out + slice_start * td->out_linesize;
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int y, x;
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for (y = slice_start; y < slice_end; ++y){
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for (x = 0; x < td->width; ++x){
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dst[x] = (uint8_t)(255.0f * FFMIN(src[x], 1.0f));
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}
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src += td->width;
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dst += td->out_linesize;
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}
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return 0;
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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* context = inlink->dst;
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SRCNNContext* srcnn_context = context->priv;
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AVFilterLink* outlink = context->outputs[0];
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AVFrame* out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
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ThreadData td;
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int nb_threads;
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DNNReturnType dnn_result;
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if (!out){
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av_log(context, 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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av_frame_copy(out, in);
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av_frame_free(&in);
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td.out = out->data[0];
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td.out_linesize = out->linesize[0];
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td.height = out->height;
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td.width = out->width;
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nb_threads = ff_filter_get_nb_threads(context);
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context->internal->execute(context, uint8_to_float, &td, NULL, FFMIN(td.height, nb_threads));
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dnn_result = (srcnn_context->dnn_module->execute_model)(srcnn_context->model);
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if (dnn_result != DNN_SUCCESS){
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av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
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return AVERROR(EIO);
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}
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context->internal->execute(context, float_to_uint8, &td, NULL, FFMIN(td.height, nb_threads));
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return ff_filter_frame(outlink, out);
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}
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static av_cold void uninit(AVFilterContext* context)
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{
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SRCNNContext* srcnn_context = context->priv;
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if (srcnn_context->dnn_module){
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(srcnn_context->dnn_module->free_model)(&srcnn_context->model);
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av_freep(&srcnn_context->dnn_module);
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}
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av_freep(&srcnn_context->input_output_buf);
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}
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static const AVFilterPad srcnn_inputs[] = {
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{
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.name = "default",
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.type = AVMEDIA_TYPE_VIDEO,
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.config_props = config_props,
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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 srcnn_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_srcnn = {
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.name = "srcnn",
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.description = NULL_IF_CONFIG_SMALL("Apply super resolution convolutional neural network to the input. Use bicubic upsamping with corresponding scaling factor before."),
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.priv_size = sizeof(SRCNNContext),
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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 = srcnn_inputs,
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.outputs = srcnn_outputs,
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.priv_class = &srcnn_class,
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.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS,
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};
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