/* * Copyright (c) 2018 Sergey Lavrushkin * * 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 super-resolution using deep convolutional networks. * https://arxiv.org/abs/1501.00092 * https://arxiv.org/abs/1609.05158 */ #include "avfilter.h" #include "formats.h" #include "internal.h" #include "libavutil/opt.h" #include "libavformat/avio.h" #include "libswscale/swscale.h" #include "dnn_interface.h" typedef enum {SRCNN, ESPCN} SRModel; typedef struct SRContext { const AVClass *class; SRModel model_type; char* model_filename; DNNBackendType backend_type; DNNModule* dnn_module; DNNModel* model; DNNData input, output; int scale_factor; struct SwsContext* sws_context; int sws_slice_h; } SRContext; #define OFFSET(x) offsetof(SRContext, x) #define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM static const AVOption sr_options[] = { { "model", "specifies what DNN model to use", OFFSET(model_type), AV_OPT_TYPE_FLAGS, { .i64 = 0 }, 0, 1, FLAGS, "model_type" }, { "srcnn", "Super-Resolution Convolutional Neural Network model (scale factor should be specified for custom SRCNN model)", 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, "model_type" }, { "espcn", "Efficient Sub-Pixel Convolutional Neural Network model", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "model_type" }, { "dnn_backend", "DNN backend used for model execution", OFFSET(backend_type), AV_OPT_TYPE_FLAGS, { .i64 = 0 }, 0, 1, FLAGS, "backend" }, { "native", "native backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, "backend" }, #if (CONFIG_LIBTENSORFLOW == 1) { "tensorflow", "tensorflow backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "backend" }, #endif {"scale_factor", "scale factor for SRCNN model", OFFSET(scale_factor), AV_OPT_TYPE_INT, { .i64 = 2 }, 2, 4, FLAGS}, { "model_filename", "path to model file specifying network architecture and its parameters", OFFSET(model_filename), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, FLAGS }, { NULL } }; AVFILTER_DEFINE_CLASS(sr); static av_cold int init(AVFilterContext* context) { SRContext* sr_context = context->priv; sr_context->dnn_module = ff_get_dnn_module(sr_context->backend_type); if (!sr_context->dnn_module){ av_log(context, AV_LOG_ERROR, "could not create DNN module for requested backend\n"); return AVERROR(ENOMEM); } if (!sr_context->model_filename){ av_log(context, AV_LOG_VERBOSE, "model file for network was not specified, using default network for x2 upsampling\n"); sr_context->scale_factor = 2; switch (sr_context->model_type){ case SRCNN: sr_context->model = (sr_context->dnn_module->load_default_model)(DNN_SRCNN); break; case ESPCN: sr_context->model = (sr_context->dnn_module->load_default_model)(DNN_ESPCN); } } else{ sr_context->model = (sr_context->dnn_module->load_model)(sr_context->model_filename); } if (!sr_context->model){ av_log(context, AV_LOG_ERROR, "could not load DNN model\n"); return AVERROR(EIO); } return 0; } static int query_formats(AVFilterContext* context) { const enum AVPixelFormat pixel_formats[] = {AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P, AV_PIX_FMT_YUV444P, AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P, AV_PIX_FMT_GRAY8, AV_PIX_FMT_NONE}; AVFilterFormats* formats_list; formats_list = ff_make_format_list(pixel_formats); if (!formats_list){ av_log(context, AV_LOG_ERROR, "could not create formats list\n"); return AVERROR(ENOMEM); } return ff_set_common_formats(context, formats_list); } static int config_props(AVFilterLink* inlink) { AVFilterContext* context = inlink->dst; SRContext* sr_context = context->priv; AVFilterLink* outlink = context->outputs[0]; DNNReturnType result; int sws_src_h, sws_src_w, sws_dst_h, sws_dst_w; switch (sr_context->model_type){ case SRCNN: sr_context->input.width = inlink->w * sr_context->scale_factor; sr_context->input.height = inlink->h * sr_context->scale_factor; break; case ESPCN: sr_context->input.width = inlink->w; sr_context->input.height = inlink->h; } sr_context->input.channels = 1; result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, &sr_context->output); if (result != DNN_SUCCESS){ av_log(context, AV_LOG_ERROR, "could not set input and output for the model\n"); return AVERROR(EIO); } else{ outlink->h = sr_context->output.height; outlink->w = sr_context->output.width; switch (sr_context->model_type){ case SRCNN: sr_context->sws_context = sws_getContext(inlink->w, inlink->h, inlink->format, outlink->w, outlink->h, outlink->format, SWS_BICUBIC, NULL, NULL, NULL); if (!sr_context->sws_context){ av_log(context, AV_LOG_ERROR, "could not create SwsContext\n"); return AVERROR(ENOMEM); } sr_context->sws_slice_h = inlink->h; break; case ESPCN: if (inlink->format == AV_PIX_FMT_GRAY8){ sr_context->sws_context = NULL; } else{ sws_src_h = sr_context->input.height; sws_src_w = sr_context->input.width; sws_dst_h = sr_context->output.height; sws_dst_w = sr_context->output.width; switch (inlink->format){ case AV_PIX_FMT_YUV420P: sws_src_h = (sws_src_h >> 1) + (sws_src_h % 2 != 0 ? 1 : 0); sws_src_w = (sws_src_w >> 1) + (sws_src_w % 2 != 0 ? 1 : 0); sws_dst_h = (sws_dst_h >> 1) + (sws_dst_h % 2 != 0 ? 1 : 0); sws_dst_w = (sws_dst_w >> 1) + (sws_dst_w % 2 != 0 ? 1 : 0); break; case AV_PIX_FMT_YUV422P: sws_src_w = (sws_src_w >> 1) + (sws_src_w % 2 != 0 ? 1 : 0); sws_dst_w = (sws_dst_w >> 1) + (sws_dst_w % 2 != 0 ? 1 : 0); break; case AV_PIX_FMT_YUV444P: break; case AV_PIX_FMT_YUV410P: sws_src_h = (sws_src_h >> 2) + (sws_src_h % 4 != 0 ? 1 : 0); sws_src_w = (sws_src_w >> 2) + (sws_src_w % 4 != 0 ? 1 : 0); sws_dst_h = (sws_dst_h >> 2) + (sws_dst_h % 4 != 0 ? 1 : 0); sws_dst_w = (sws_dst_w >> 2) + (sws_dst_w % 4 != 0 ? 1 : 0); break; case AV_PIX_FMT_YUV411P: sws_src_w = (sws_src_w >> 2) + (sws_src_w % 4 != 0 ? 1 : 0); sws_dst_w = (sws_dst_w >> 2) + (sws_dst_w % 4 != 0 ? 1 : 0); break; default: av_log(context, AV_LOG_ERROR, "could not create SwsContext for input pixel format"); return AVERROR(EIO); } sr_context->sws_context = sws_getContext(sws_src_w, sws_src_h, AV_PIX_FMT_GRAY8, sws_dst_w, sws_dst_h, AV_PIX_FMT_GRAY8, SWS_BICUBIC, NULL, NULL, NULL); if (!sr_context->sws_context){ av_log(context, AV_LOG_ERROR, "could not create SwsContext\n"); return AVERROR(ENOMEM); } sr_context->sws_slice_h = sws_src_h; } } return 0; } } typedef struct ThreadData{ uint8_t* data; int data_linesize, height, width; } ThreadData; static int uint8_to_float(AVFilterContext* context, void* arg, int jobnr, int nb_jobs) { SRContext* sr_context = context->priv; const ThreadData* td = arg; const int slice_start = (td->height * jobnr ) / nb_jobs; const int slice_end = (td->height * (jobnr + 1)) / nb_jobs; const uint8_t* src = td->data + slice_start * td->data_linesize; float* dst = sr_context->input.data + slice_start * td->width; int y, x; for (y = slice_start; y < slice_end; ++y){ for (x = 0; x < td->width; ++x){ dst[x] = (float)src[x] / 255.0f; } src += td->data_linesize; dst += td->width; } return 0; } static int float_to_uint8(AVFilterContext* context, void* arg, int jobnr, int nb_jobs) { SRContext* sr_context = context->priv; const ThreadData* td = arg; const int slice_start = (td->height * jobnr ) / nb_jobs; const int slice_end = (td->height * (jobnr + 1)) / nb_jobs; const float* src = sr_context->output.data + slice_start * td->width; uint8_t* dst = td->data + slice_start * td->data_linesize; int y, x; for (y = slice_start; y < slice_end; ++y){ for (x = 0; x < td->width; ++x){ dst[x] = (uint8_t)(255.0f * FFMIN(src[x], 1.0f)); } src += td->width; dst += td->data_linesize; } return 0; } static int filter_frame(AVFilterLink* inlink, AVFrame* in) { AVFilterContext* context = inlink->dst; SRContext* sr_context = context->priv; AVFilterLink* outlink = context->outputs[0]; AVFrame* out = ff_get_video_buffer(outlink, outlink->w, outlink->h); ThreadData td; int nb_threads; DNNReturnType dnn_result; if (!out){ av_log(context, AV_LOG_ERROR, "could not allocate memory for output frame\n"); av_frame_free(&in); return AVERROR(ENOMEM); } av_frame_copy_props(out, in); out->height = sr_context->output.height; out->width = sr_context->output.width; switch (sr_context->model_type){ case SRCNN: sws_scale(sr_context->sws_context, (const uint8_t **)in->data, in->linesize, 0, sr_context->sws_slice_h, out->data, out->linesize); td.data = out->data[0]; td.data_linesize = out->linesize[0]; td.height = out->height; td.width = out->width; break; case ESPCN: if (sr_context->sws_context){ sws_scale(sr_context->sws_context, (const uint8_t **)(in->data + 1), in->linesize + 1, 0, sr_context->sws_slice_h, out->data + 1, out->linesize + 1); sws_scale(sr_context->sws_context, (const uint8_t **)(in->data + 2), in->linesize + 2, 0, sr_context->sws_slice_h, out->data + 2, out->linesize + 2); } td.data = in->data[0]; td.data_linesize = in->linesize[0]; td.height = in->height; td.width = in->width; } nb_threads = ff_filter_get_nb_threads(context); context->internal->execute(context, uint8_to_float, &td, NULL, FFMIN(td.height, nb_threads)); av_frame_free(&in); dnn_result = (sr_context->dnn_module->execute_model)(sr_context->model); if (dnn_result != DNN_SUCCESS){ av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n"); return AVERROR(EIO); } td.data = out->data[0]; td.data_linesize = out->linesize[0]; td.height = out->height; td.width = out->width; context->internal->execute(context, float_to_uint8, &td, NULL, FFMIN(td.height, nb_threads)); return ff_filter_frame(outlink, out); } static av_cold void uninit(AVFilterContext* context) { SRContext* sr_context = context->priv; if (sr_context->dnn_module){ (sr_context->dnn_module->free_model)(&sr_context->model); av_freep(&sr_context->dnn_module); } if (sr_context->sws_context){ sws_freeContext(sr_context->sws_context); } } static const AVFilterPad sr_inputs[] = { { .name = "default", .type = AVMEDIA_TYPE_VIDEO, .config_props = config_props, .filter_frame = filter_frame, }, { NULL } }; static const AVFilterPad sr_outputs[] = { { .name = "default", .type = AVMEDIA_TYPE_VIDEO, }, { NULL } }; AVFilter ff_vf_sr = { .name = "sr", .description = NULL_IF_CONFIG_SMALL("Apply DNN-based image super resolution to the input."), .priv_size = sizeof(SRContext), .init = init, .uninit = uninit, .query_formats = query_formats, .inputs = sr_inputs, .outputs = sr_outputs, .priv_class = &sr_class, .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS, };