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/*
* 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
*/
#include "dnn_filter_common.h"
#include "libavutil/avstring.h"
#include "libavutil/mem.h"
#define MAX_SUPPORTED_OUTPUTS_NB 4
static char **separate_output_names(const char *expr, const char *val_sep, int *separated_nb)
{
char *val, **parsed_vals = NULL;
int val_num = 0;
if (!expr || !val_sep || !separated_nb) {
return NULL;
}
parsed_vals = av_calloc(MAX_SUPPORTED_OUTPUTS_NB, sizeof(*parsed_vals));
if (!parsed_vals) {
return NULL;
}
do {
val = av_get_token(&expr, val_sep);
if(val) {
parsed_vals[val_num] = val;
val_num++;
}
if (*expr) {
expr++;
}
} while(*expr);
parsed_vals[val_num] = NULL;
*separated_nb = val_num;
return parsed_vals;
}
int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx)
{
DNNBackendType backend = ctx->backend_type;
if (!ctx->model_filename) {
av_log(filter_ctx, AV_LOG_ERROR, "model file for network is not specified\n");
return AVERROR(EINVAL);
}
if (backend == DNN_TH) {
if (ctx->model_inputname)
av_log(filter_ctx, AV_LOG_WARNING, "LibTorch backend do not require inputname, "\
"inputname will be ignored.\n");
if (ctx->model_outputnames)
av_log(filter_ctx, AV_LOG_WARNING, "LibTorch backend do not require outputname(s), "\
"all outputname(s) will be ignored.\n");
ctx->nb_outputs = 1;
} else if (backend == DNN_TF) {
if (!ctx->model_inputname) {
av_log(filter_ctx, AV_LOG_ERROR, "input name of the model network is not specified\n");
return AVERROR(EINVAL);
}
ctx->model_outputnames = separate_output_names(ctx->model_outputnames_string, "&", &ctx->nb_outputs);
if (!ctx->model_outputnames) {
av_log(filter_ctx, AV_LOG_ERROR, "could not parse model output names\n");
return AVERROR(EINVAL);
}
}
ctx->dnn_module = ff_get_dnn_module(ctx->backend_type, filter_ctx);
if (!ctx->dnn_module) {
av_log(filter_ctx, AV_LOG_ERROR, "could not create DNN module for requested backend\n");
return AVERROR(ENOMEM);
}
if (!ctx->dnn_module->load_model) {
av_log(filter_ctx, AV_LOG_ERROR, "load_model for network is not specified\n");
return AVERROR(EINVAL);
}
ctx->model = (ctx->dnn_module->load_model)(ctx->model_filename, func_type, ctx->backend_options, filter_ctx);
if (!ctx->model) {
av_log(filter_ctx, AV_LOG_ERROR, "could not load DNN model\n");
return AVERROR(EINVAL);
}
return 0;
}
int ff_dnn_set_frame_proc(DnnContext *ctx, FramePrePostProc pre_proc, FramePrePostProc post_proc)
{
ctx->model->frame_pre_proc = pre_proc;
ctx->model->frame_post_proc = post_proc;
return 0;
}
int ff_dnn_set_detect_post_proc(DnnContext *ctx, DetectPostProc post_proc)
{
ctx->model->detect_post_proc = post_proc;
return 0;
}
int ff_dnn_set_classify_post_proc(DnnContext *ctx, ClassifyPostProc post_proc)
{
ctx->model->classify_post_proc = post_proc;
return 0;
}
int ff_dnn_get_input(DnnContext *ctx, DNNData *input)
{
return ctx->model->get_input(ctx->model->model, input, ctx->model_inputname);
}
int ff_dnn_get_output(DnnContext *ctx, int input_width, int input_height, int *output_width, int *output_height)
{
char * output_name = ctx->model_outputnames && ctx->backend_type != DNN_TH ?
ctx->model_outputnames[0] : NULL;
return ctx->model->get_output(ctx->model->model, ctx->model_inputname, input_width, input_height,
(const char *)output_name, output_width, output_height);
}
int ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame)
{
DNNExecBaseParams exec_params = {
.input_name = ctx->model_inputname,
.output_names = (const char **)ctx->model_outputnames,
.nb_output = ctx->nb_outputs,
.in_frame = in_frame,
.out_frame = out_frame,
};
return (ctx->dnn_module->execute_model)(ctx->model, &exec_params);
}
int ff_dnn_execute_model_classification(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame, const char *target)
{
DNNExecClassificationParams class_params = {
{
.input_name = ctx->model_inputname,
.output_names = (const char **)ctx->model_outputnames,
.nb_output = ctx->nb_outputs,
.in_frame = in_frame,
.out_frame = out_frame,
},
.target = target,
};
return (ctx->dnn_module->execute_model)(ctx->model, &class_params.base);
}
DNNAsyncStatusType ff_dnn_get_result(DnnContext *ctx, AVFrame **in_frame, AVFrame **out_frame)
{
return (ctx->dnn_module->get_result)(ctx->model, in_frame, out_frame);
}
int ff_dnn_flush(DnnContext *ctx)
{
return (ctx->dnn_module->flush)(ctx->model);
}
void ff_dnn_uninit(DnnContext *ctx)
{
if (ctx->dnn_module) {
(ctx->dnn_module->free_model)(&ctx->model);
}
if (ctx->model_outputnames) {
for (int i = 0; i < ctx->nb_outputs; i++)
av_free(ctx->model_outputnames[i]);
av_freep(&ctx->model_outputnames);
}
}
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