293 lines
12 KiB
C
293 lines
12 KiB
C
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/*
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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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* https://arxiv.org/abs/1609.05158
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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 "libavutil/pixdesc.h"
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#include "libavformat/avio.h"
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#include "libswscale/swscale.h"
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#include "dnn_interface.h"
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typedef struct SRContext {
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const AVClass *class;
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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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DNNData input;
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DNNData output;
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int scale_factor;
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struct SwsContext *sws_contexts[3];
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int sws_slice_h, sws_input_linesize, sws_output_linesize;
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} SRContext;
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#define OFFSET(x) offsetof(SRContext, x)
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#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
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static const AVOption sr_options[] = {
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{ "dnn_backend", "DNN backend used for model execution", 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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{ "scale_factor", "scale factor for SRCNN model", OFFSET(scale_factor), AV_OPT_TYPE_INT, { .i64 = 2 }, 2, 4, FLAGS },
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{ "model", "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(sr);
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static av_cold int init(AVFilterContext *context)
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{
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SRContext *sr_context = context->priv;
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sr_context->dnn_module = ff_get_dnn_module(sr_context->backend_type);
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if (!sr_context->dnn_module){
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av_log(context, 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 (!sr_context->model_filename){
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av_log(context, AV_LOG_ERROR, "model file for network was not specified\n");
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return AVERROR(EIO);
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}
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if (!sr_context->dnn_module->load_model) {
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av_log(context, AV_LOG_ERROR, "load_model for network was not specified\n");
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return AVERROR(EIO);
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}
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sr_context->model = (sr_context->dnn_module->load_model)(sr_context->model_filename);
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if (!sr_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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sr_context->input.dt = DNN_FLOAT;
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sr_context->sws_contexts[0] = NULL;
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sr_context->sws_contexts[1] = NULL;
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sr_context->sws_contexts[2] = NULL;
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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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SRContext *sr_context = context->priv;
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AVFilterLink *outlink = context->outputs[0];
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DNNReturnType result;
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int sws_src_h, sws_src_w, sws_dst_h, sws_dst_w;
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const char *model_output_name = "y";
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sr_context->input.width = inlink->w * sr_context->scale_factor;
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sr_context->input.height = inlink->h * sr_context->scale_factor;
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sr_context->input.channels = 1;
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result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", &model_output_name, 1);
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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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result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output, 1);
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if (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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if (sr_context->input.height != sr_context->output.height || sr_context->input.width != sr_context->output.width){
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sr_context->input.width = inlink->w;
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sr_context->input.height = inlink->h;
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result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", &model_output_name, 1);
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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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result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output, 1);
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if (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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sr_context->scale_factor = 0;
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}
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outlink->h = sr_context->output.height;
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outlink->w = sr_context->output.width;
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sr_context->sws_contexts[1] = sws_getContext(sr_context->input.width, sr_context->input.height, AV_PIX_FMT_GRAY8,
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sr_context->input.width, sr_context->input.height, AV_PIX_FMT_GRAYF32,
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0, NULL, NULL, NULL);
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sr_context->sws_input_linesize = sr_context->input.width << 2;
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sr_context->sws_contexts[2] = sws_getContext(sr_context->output.width, sr_context->output.height, AV_PIX_FMT_GRAYF32,
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sr_context->output.width, sr_context->output.height, AV_PIX_FMT_GRAY8,
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0, NULL, NULL, NULL);
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sr_context->sws_output_linesize = sr_context->output.width << 2;
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if (!sr_context->sws_contexts[1] || !sr_context->sws_contexts[2]){
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av_log(context, AV_LOG_ERROR, "could not create SwsContext for conversions\n");
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return AVERROR(ENOMEM);
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}
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if (sr_context->scale_factor){
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sr_context->sws_contexts[0] = sws_getContext(inlink->w, inlink->h, inlink->format,
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outlink->w, outlink->h, outlink->format,
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SWS_BICUBIC, NULL, NULL, NULL);
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if (!sr_context->sws_contexts[0]){
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av_log(context, AV_LOG_ERROR, "could not create SwsContext for scaling\n");
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return AVERROR(ENOMEM);
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}
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sr_context->sws_slice_h = inlink->h;
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} else {
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if (inlink->format != AV_PIX_FMT_GRAY8){
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const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
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sws_src_h = AV_CEIL_RSHIFT(sr_context->input.height, desc->log2_chroma_h);
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sws_src_w = AV_CEIL_RSHIFT(sr_context->input.width, desc->log2_chroma_w);
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sws_dst_h = AV_CEIL_RSHIFT(sr_context->output.height, desc->log2_chroma_h);
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sws_dst_w = AV_CEIL_RSHIFT(sr_context->output.width, desc->log2_chroma_w);
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sr_context->sws_contexts[0] = sws_getContext(sws_src_w, sws_src_h, AV_PIX_FMT_GRAY8,
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sws_dst_w, sws_dst_h, AV_PIX_FMT_GRAY8,
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SWS_BICUBIC, NULL, NULL, NULL);
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if (!sr_context->sws_contexts[0]){
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av_log(context, AV_LOG_ERROR, "could not create SwsContext for scaling\n");
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return AVERROR(ENOMEM);
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}
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sr_context->sws_slice_h = sws_src_h;
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}
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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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SRContext *sr_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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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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out->height = sr_context->output.height;
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out->width = sr_context->output.width;
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if (sr_context->scale_factor){
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sws_scale(sr_context->sws_contexts[0], (const uint8_t **)in->data, in->linesize,
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0, sr_context->sws_slice_h, out->data, out->linesize);
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sws_scale(sr_context->sws_contexts[1], (const uint8_t **)out->data, out->linesize,
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0, out->height, (uint8_t * const*)(&sr_context->input.data),
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(const int [4]){sr_context->sws_input_linesize, 0, 0, 0});
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} else {
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if (sr_context->sws_contexts[0]){
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sws_scale(sr_context->sws_contexts[0], (const uint8_t **)(in->data + 1), in->linesize + 1,
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0, sr_context->sws_slice_h, out->data + 1, out->linesize + 1);
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sws_scale(sr_context->sws_contexts[0], (const uint8_t **)(in->data + 2), in->linesize + 2,
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0, sr_context->sws_slice_h, out->data + 2, out->linesize + 2);
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}
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sws_scale(sr_context->sws_contexts[1], (const uint8_t **)in->data, in->linesize,
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0, in->height, (uint8_t * const*)(&sr_context->input.data),
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(const int [4]){sr_context->sws_input_linesize, 0, 0, 0});
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}
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av_frame_free(&in);
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dnn_result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output, 1);
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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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sws_scale(sr_context->sws_contexts[2], (const uint8_t *[4]){(const uint8_t *)sr_context->output.data, 0, 0, 0},
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(const int[4]){sr_context->sws_output_linesize, 0, 0, 0},
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0, out->height, (uint8_t * const*)out->data, out->linesize);
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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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int i;
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SRContext *sr_context = context->priv;
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if (sr_context->dnn_module){
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(sr_context->dnn_module->free_model)(&sr_context->model);
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av_freep(&sr_context->dnn_module);
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}
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for (i = 0; i < 3; ++i){
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sws_freeContext(sr_context->sws_contexts[i]);
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}
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}
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static const AVFilterPad sr_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 sr_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_sr = {
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.name = "sr",
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.description = NULL_IF_CONFIG_SMALL("Apply DNN-based image super resolution to the input."),
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.priv_size = sizeof(SRContext),
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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 = sr_inputs,
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.outputs = sr_outputs,
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.priv_class = &sr_class,
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};
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