521 lines
19 KiB
C
Executable File
521 lines
19 KiB
C
Executable File
/*
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* Copyright (c) 2019 Guo Yejun
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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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* implementing a generic image processing filter using deep learning networks.
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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 "libavutil/pixdesc.h"
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#include "libavutil/avassert.h"
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#include "libavutil/imgutils.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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#include "libswscale/swscale.h"
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typedef struct DnnProcessingContext {
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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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char *model_inputname;
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char *model_outputname;
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DNNModule *dnn_module;
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DNNModel *model;
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// input & output of the model at execution time
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DNNData input;
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DNNData output;
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struct SwsContext *sws_gray8_to_grayf32;
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struct SwsContext *sws_grayf32_to_gray8;
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struct SwsContext *sws_uv_scale;
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int sws_uv_height;
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} DnnProcessingContext;
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#define OFFSET(x) offsetof(DnnProcessingContext, x)
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#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
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static const AVOption dnn_processing_options[] = {
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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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{ "input", "input name of the model", OFFSET(model_inputname), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
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{ "output", "output name of the model", OFFSET(model_outputname), 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(dnn_processing);
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static av_cold int init(AVFilterContext *context)
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{
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DnnProcessingContext *ctx = context->priv;
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if (!ctx->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 (!ctx->model_inputname) {
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av_log(ctx, AV_LOG_ERROR, "input name of the model network is not specified\n");
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return AVERROR(EINVAL);
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}
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if (!ctx->model_outputname) {
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av_log(ctx, AV_LOG_ERROR, "output name of the model network is not specified\n");
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return AVERROR(EINVAL);
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}
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ctx->dnn_module = ff_get_dnn_module(ctx->backend_type);
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if (!ctx->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 (!ctx->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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ctx->model = (ctx->dnn_module->load_model)(ctx->model_filename);
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if (!ctx->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 int query_formats(AVFilterContext *context)
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{
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static const enum AVPixelFormat pix_fmts[] = {
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AV_PIX_FMT_RGB24, AV_PIX_FMT_BGR24,
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AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAYF32,
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AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
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AV_PIX_FMT_YUV444P, AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
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AV_PIX_FMT_NONE
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};
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AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
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return ff_set_common_formats(context, fmts_list);
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}
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#define LOG_FORMAT_CHANNEL_MISMATCH() \
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av_log(ctx, AV_LOG_ERROR, \
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"the frame's format %s does not match " \
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"the model input channel %d\n", \
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av_get_pix_fmt_name(fmt), \
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model_input->channels);
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static int check_modelinput_inlink(const DNNData *model_input, const AVFilterLink *inlink)
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{
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AVFilterContext *ctx = inlink->dst;
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enum AVPixelFormat fmt = inlink->format;
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// the design is to add explicit scale filter before this filter
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if (model_input->height != -1 && model_input->height != inlink->h) {
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av_log(ctx, AV_LOG_ERROR, "the model requires frame height %d but got %d\n",
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model_input->height, inlink->h);
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return AVERROR(EIO);
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}
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if (model_input->width != -1 && model_input->width != inlink->w) {
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av_log(ctx, AV_LOG_ERROR, "the model requires frame width %d but got %d\n",
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model_input->width, inlink->w);
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return AVERROR(EIO);
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}
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switch (fmt) {
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case AV_PIX_FMT_RGB24:
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case AV_PIX_FMT_BGR24:
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if (model_input->channels != 3) {
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LOG_FORMAT_CHANNEL_MISMATCH();
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return AVERROR(EIO);
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}
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if (model_input->dt != DNN_FLOAT && model_input->dt != DNN_UINT8) {
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av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type as float32 and uint8.\n");
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return AVERROR(EIO);
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}
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return 0;
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case AV_PIX_FMT_GRAY8:
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if (model_input->channels != 1) {
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LOG_FORMAT_CHANNEL_MISMATCH();
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return AVERROR(EIO);
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}
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if (model_input->dt != DNN_UINT8) {
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av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type uint8.\n");
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return AVERROR(EIO);
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}
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return 0;
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case AV_PIX_FMT_GRAYF32:
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case AV_PIX_FMT_YUV420P:
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case AV_PIX_FMT_YUV422P:
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case AV_PIX_FMT_YUV444P:
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case AV_PIX_FMT_YUV410P:
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case AV_PIX_FMT_YUV411P:
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if (model_input->channels != 1) {
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LOG_FORMAT_CHANNEL_MISMATCH();
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return AVERROR(EIO);
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}
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if (model_input->dt != DNN_FLOAT) {
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av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type float32.\n");
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return AVERROR(EIO);
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}
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return 0;
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default:
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av_log(ctx, AV_LOG_ERROR, "%s not supported.\n", av_get_pix_fmt_name(fmt));
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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 config_input(AVFilterLink *inlink)
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{
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AVFilterContext *context = inlink->dst;
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DnnProcessingContext *ctx = context->priv;
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DNNReturnType result;
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DNNData model_input;
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int check;
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result = ctx->model->get_input(ctx->model->model, &model_input, ctx->model_inputname);
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if (result != DNN_SUCCESS) {
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av_log(ctx, AV_LOG_ERROR, "could not get input from the model\n");
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return AVERROR(EIO);
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}
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check = check_modelinput_inlink(&model_input, inlink);
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if (check != 0) {
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return check;
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}
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ctx->input.width = inlink->w;
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ctx->input.height = inlink->h;
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ctx->input.channels = model_input.channels;
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ctx->input.dt = model_input.dt;
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result = (ctx->model->set_input_output)(ctx->model->model,
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&ctx->input, ctx->model_inputname,
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(const char **)&ctx->model_outputname, 1);
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if (result != DNN_SUCCESS) {
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av_log(ctx, 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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return 0;
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}
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static int prepare_sws_context(AVFilterLink *outlink)
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{
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AVFilterContext *context = outlink->src;
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DnnProcessingContext *ctx = context->priv;
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AVFilterLink *inlink = context->inputs[0];
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enum AVPixelFormat fmt = inlink->format;
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DNNDataType input_dt = ctx->input.dt;
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DNNDataType output_dt = ctx->output.dt;
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switch (fmt) {
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case AV_PIX_FMT_RGB24:
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case AV_PIX_FMT_BGR24:
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if (input_dt == DNN_FLOAT) {
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ctx->sws_gray8_to_grayf32 = sws_getContext(inlink->w * 3,
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inlink->h,
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AV_PIX_FMT_GRAY8,
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inlink->w * 3,
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inlink->h,
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AV_PIX_FMT_GRAYF32,
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0, NULL, NULL, NULL);
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}
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if (output_dt == DNN_FLOAT) {
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ctx->sws_grayf32_to_gray8 = sws_getContext(outlink->w * 3,
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outlink->h,
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AV_PIX_FMT_GRAYF32,
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outlink->w * 3,
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outlink->h,
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AV_PIX_FMT_GRAY8,
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0, NULL, NULL, NULL);
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}
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return 0;
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case AV_PIX_FMT_YUV420P:
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case AV_PIX_FMT_YUV422P:
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case AV_PIX_FMT_YUV444P:
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case AV_PIX_FMT_YUV410P:
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case AV_PIX_FMT_YUV411P:
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av_assert0(input_dt == DNN_FLOAT);
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av_assert0(output_dt == DNN_FLOAT);
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ctx->sws_gray8_to_grayf32 = sws_getContext(inlink->w,
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inlink->h,
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AV_PIX_FMT_GRAY8,
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inlink->w,
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inlink->h,
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AV_PIX_FMT_GRAYF32,
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0, NULL, NULL, NULL);
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ctx->sws_grayf32_to_gray8 = sws_getContext(outlink->w,
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outlink->h,
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AV_PIX_FMT_GRAYF32,
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outlink->w,
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outlink->h,
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AV_PIX_FMT_GRAY8,
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0, NULL, NULL, NULL);
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if (inlink->w != outlink->w || inlink->h != outlink->h) {
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const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(fmt);
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int sws_src_h = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
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int sws_src_w = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
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int sws_dst_h = AV_CEIL_RSHIFT(outlink->h, desc->log2_chroma_h);
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int sws_dst_w = AV_CEIL_RSHIFT(outlink->w, desc->log2_chroma_w);
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ctx->sws_uv_scale = 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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ctx->sws_uv_height = sws_src_h;
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}
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return 0;
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default:
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//do nothing
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break;
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}
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return 0;
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}
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static int config_output(AVFilterLink *outlink)
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{
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AVFilterContext *context = outlink->src;
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DnnProcessingContext *ctx = context->priv;
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DNNReturnType result;
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// have a try run in case that the dnn model resize the frame
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result = (ctx->dnn_module->execute_model)(ctx->model, &ctx->output, 1);
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if (result != DNN_SUCCESS){
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av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
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return AVERROR(EIO);
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}
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outlink->w = ctx->output.width;
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outlink->h = ctx->output.height;
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prepare_sws_context(outlink);
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return 0;
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}
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static int copy_from_frame_to_dnn(DnnProcessingContext *ctx, const AVFrame *frame)
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{
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int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
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DNNData *dnn_input = &ctx->input;
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switch (frame->format) {
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case AV_PIX_FMT_RGB24:
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case AV_PIX_FMT_BGR24:
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if (dnn_input->dt == DNN_FLOAT) {
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sws_scale(ctx->sws_gray8_to_grayf32, (const uint8_t **)frame->data, frame->linesize,
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0, frame->height, (uint8_t * const*)(&dnn_input->data),
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(const int [4]){frame->width * 3 * sizeof(float), 0, 0, 0});
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} else {
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av_assert0(dnn_input->dt == DNN_UINT8);
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av_image_copy_plane(dnn_input->data, bytewidth,
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frame->data[0], frame->linesize[0],
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bytewidth, frame->height);
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}
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return 0;
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case AV_PIX_FMT_GRAY8:
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case AV_PIX_FMT_GRAYF32:
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av_image_copy_plane(dnn_input->data, bytewidth,
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frame->data[0], frame->linesize[0],
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bytewidth, frame->height);
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return 0;
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case AV_PIX_FMT_YUV420P:
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case AV_PIX_FMT_YUV422P:
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case AV_PIX_FMT_YUV444P:
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case AV_PIX_FMT_YUV410P:
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case AV_PIX_FMT_YUV411P:
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sws_scale(ctx->sws_gray8_to_grayf32, (const uint8_t **)frame->data, frame->linesize,
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0, frame->height, (uint8_t * const*)(&dnn_input->data),
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(const int [4]){frame->width * sizeof(float), 0, 0, 0});
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return 0;
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default:
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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 copy_from_dnn_to_frame(DnnProcessingContext *ctx, AVFrame *frame)
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{
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int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
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DNNData *dnn_output = &ctx->output;
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switch (frame->format) {
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case AV_PIX_FMT_RGB24:
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case AV_PIX_FMT_BGR24:
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if (dnn_output->dt == DNN_FLOAT) {
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sws_scale(ctx->sws_grayf32_to_gray8, (const uint8_t *[4]){(const uint8_t *)dnn_output->data, 0, 0, 0},
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(const int[4]){frame->width * 3 * sizeof(float), 0, 0, 0},
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0, frame->height, (uint8_t * const*)frame->data, frame->linesize);
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} else {
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av_assert0(dnn_output->dt == DNN_UINT8);
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av_image_copy_plane(frame->data[0], frame->linesize[0],
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dnn_output->data, bytewidth,
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bytewidth, frame->height);
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}
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return 0;
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case AV_PIX_FMT_GRAY8:
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// it is possible that data type of dnn output is float32,
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// need to add support for such case when needed.
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av_assert0(dnn_output->dt == DNN_UINT8);
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av_image_copy_plane(frame->data[0], frame->linesize[0],
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dnn_output->data, bytewidth,
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bytewidth, frame->height);
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return 0;
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case AV_PIX_FMT_GRAYF32:
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av_assert0(dnn_output->dt == DNN_FLOAT);
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av_image_copy_plane(frame->data[0], frame->linesize[0],
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dnn_output->data, bytewidth,
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bytewidth, frame->height);
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return 0;
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case AV_PIX_FMT_YUV420P:
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case AV_PIX_FMT_YUV422P:
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case AV_PIX_FMT_YUV444P:
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case AV_PIX_FMT_YUV410P:
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case AV_PIX_FMT_YUV411P:
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sws_scale(ctx->sws_grayf32_to_gray8, (const uint8_t *[4]){(const uint8_t *)dnn_output->data, 0, 0, 0},
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(const int[4]){frame->width * sizeof(float), 0, 0, 0},
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0, frame->height, (uint8_t * const*)frame->data, frame->linesize);
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return 0;
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default:
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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 av_always_inline int isPlanarYUV(enum AVPixelFormat pix_fmt)
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{
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const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(pix_fmt);
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av_assert0(desc);
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return !(desc->flags & AV_PIX_FMT_FLAG_RGB) && desc->nb_components == 3;
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}
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static int copy_uv_planes(DnnProcessingContext *ctx, AVFrame *out, const AVFrame *in)
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{
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const AVPixFmtDescriptor *desc;
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int uv_height;
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if (!ctx->sws_uv_scale) {
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av_assert0(in->height == out->height && in->width == out->width);
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desc = av_pix_fmt_desc_get(in->format);
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uv_height = AV_CEIL_RSHIFT(in->height, desc->log2_chroma_h);
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for (int i = 1; i < 3; ++i) {
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int bytewidth = av_image_get_linesize(in->format, in->width, i);
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av_image_copy_plane(out->data[i], out->linesize[i],
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in->data[i], in->linesize[i],
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bytewidth, uv_height);
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}
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} else {
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sws_scale(ctx->sws_uv_scale, (const uint8_t **)(in->data + 1), in->linesize + 1,
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0, ctx->sws_uv_height, out->data + 1, out->linesize + 1);
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sws_scale(ctx->sws_uv_scale, (const uint8_t **)(in->data + 2), in->linesize + 2,
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0, ctx->sws_uv_height, out->data + 2, out->linesize + 2);
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int filter_frame(AVFilterLink *inlink, AVFrame *in)
|
|
{
|
|
AVFilterContext *context = inlink->dst;
|
|
AVFilterLink *outlink = context->outputs[0];
|
|
DnnProcessingContext *ctx = context->priv;
|
|
DNNReturnType dnn_result;
|
|
AVFrame *out;
|
|
|
|
copy_from_frame_to_dnn(ctx, in);
|
|
|
|
dnn_result = (ctx->dnn_module->execute_model)(ctx->model, &ctx->output, 1);
|
|
if (dnn_result != DNN_SUCCESS){
|
|
av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
|
|
av_frame_free(&in);
|
|
return AVERROR(EIO);
|
|
}
|
|
|
|
out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
|
|
if (!out) {
|
|
av_frame_free(&in);
|
|
return AVERROR(ENOMEM);
|
|
}
|
|
|
|
av_frame_copy_props(out, in);
|
|
copy_from_dnn_to_frame(ctx, out);
|
|
|
|
if (isPlanarYUV(in->format))
|
|
copy_uv_planes(ctx, out, in);
|
|
|
|
av_frame_free(&in);
|
|
return ff_filter_frame(outlink, out);
|
|
}
|
|
|
|
static av_cold void uninit(AVFilterContext *ctx)
|
|
{
|
|
DnnProcessingContext *context = ctx->priv;
|
|
|
|
sws_freeContext(context->sws_gray8_to_grayf32);
|
|
sws_freeContext(context->sws_grayf32_to_gray8);
|
|
sws_freeContext(context->sws_uv_scale);
|
|
|
|
if (context->dnn_module)
|
|
(context->dnn_module->free_model)(&context->model);
|
|
|
|
av_freep(&context->dnn_module);
|
|
}
|
|
|
|
static const AVFilterPad dnn_processing_inputs[] = {
|
|
{
|
|
.name = "default",
|
|
.type = AVMEDIA_TYPE_VIDEO,
|
|
.config_props = config_input,
|
|
.filter_frame = filter_frame,
|
|
},
|
|
{ NULL }
|
|
};
|
|
|
|
static const AVFilterPad dnn_processing_outputs[] = {
|
|
{
|
|
.name = "default",
|
|
.type = AVMEDIA_TYPE_VIDEO,
|
|
.config_props = config_output,
|
|
},
|
|
{ NULL }
|
|
};
|
|
|
|
AVFilter ff_vf_dnn_processing = {
|
|
.name = "dnn_processing",
|
|
.description = NULL_IF_CONFIG_SMALL("Apply DNN processing filter to the input."),
|
|
.priv_size = sizeof(DnnProcessingContext),
|
|
.init = init,
|
|
.uninit = uninit,
|
|
.query_formats = query_formats,
|
|
.inputs = dnn_processing_inputs,
|
|
.outputs = dnn_processing_outputs,
|
|
.priv_class = &dnn_processing_class,
|
|
};
|