ffmpeg/libavfilter/vf_dnn_processing.c
Shubhanshu Saxena 70b4dca054 libavfilter: Remove synchronous functions from DNN filters
This commit removes the unused sync mode specific code from the DNN
filters since the sync and async mode are now unified from the
filters' perspective.

Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
2021-08-28 16:19:07 +08:00

379 lines
13 KiB
C

/*
* Copyright (c) 2019 Guo Yejun
*
* 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
* implementing a generic image processing filter using deep learning networks.
*/
#include "libavformat/avio.h"
#include "libavutil/opt.h"
#include "libavutil/pixdesc.h"
#include "libavutil/avassert.h"
#include "libavutil/imgutils.h"
#include "filters.h"
#include "dnn_filter_common.h"
#include "formats.h"
#include "internal.h"
#include "libswscale/swscale.h"
#include "libavutil/time.h"
typedef struct DnnProcessingContext {
const AVClass *class;
DnnContext dnnctx;
struct SwsContext *sws_uv_scale;
int sws_uv_height;
} DnnProcessingContext;
#define OFFSET(x) offsetof(DnnProcessingContext, dnnctx.x)
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
static const AVOption dnn_processing_options[] = {
{ "dnn_backend", "DNN backend", OFFSET(backend_type), AV_OPT_TYPE_INT, { .i64 = 0 }, INT_MIN, INT_MAX, 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
#if (CONFIG_LIBOPENVINO == 1)
{ "openvino", "openvino backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 2 }, 0, 0, FLAGS, "backend" },
#endif
DNN_COMMON_OPTIONS
{ NULL }
};
AVFILTER_DEFINE_CLASS(dnn_processing);
static av_cold int init(AVFilterContext *context)
{
DnnProcessingContext *ctx = context->priv;
return ff_dnn_init(&ctx->dnnctx, DFT_PROCESS_FRAME, context);
}
static int query_formats(AVFilterContext *context)
{
static const enum AVPixelFormat pix_fmts[] = {
AV_PIX_FMT_RGB24, AV_PIX_FMT_BGR24,
AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAYF32,
AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
AV_PIX_FMT_YUV444P, AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
AV_PIX_FMT_NV12,
AV_PIX_FMT_NONE
};
return ff_set_common_formats_from_list(context, pix_fmts);
}
#define LOG_FORMAT_CHANNEL_MISMATCH() \
av_log(ctx, AV_LOG_ERROR, \
"the frame's format %s does not match " \
"the model input channel %d\n", \
av_get_pix_fmt_name(fmt), \
model_input->channels);
static int check_modelinput_inlink(const DNNData *model_input, const AVFilterLink *inlink)
{
AVFilterContext *ctx = inlink->dst;
enum AVPixelFormat fmt = inlink->format;
// the design is to add explicit scale filter before this filter
if (model_input->height != -1 && model_input->height != inlink->h) {
av_log(ctx, AV_LOG_ERROR, "the model requires frame height %d but got %d\n",
model_input->height, inlink->h);
return AVERROR(EIO);
}
if (model_input->width != -1 && model_input->width != inlink->w) {
av_log(ctx, AV_LOG_ERROR, "the model requires frame width %d but got %d\n",
model_input->width, inlink->w);
return AVERROR(EIO);
}
if (model_input->dt != DNN_FLOAT) {
avpriv_report_missing_feature(ctx, "data type rather than DNN_FLOAT");
return AVERROR(EIO);
}
switch (fmt) {
case AV_PIX_FMT_RGB24:
case AV_PIX_FMT_BGR24:
if (model_input->channels != 3) {
LOG_FORMAT_CHANNEL_MISMATCH();
return AVERROR(EIO);
}
return 0;
case AV_PIX_FMT_GRAYF32:
case AV_PIX_FMT_YUV420P:
case AV_PIX_FMT_YUV422P:
case AV_PIX_FMT_YUV444P:
case AV_PIX_FMT_YUV410P:
case AV_PIX_FMT_YUV411P:
case AV_PIX_FMT_NV12:
if (model_input->channels != 1) {
LOG_FORMAT_CHANNEL_MISMATCH();
return AVERROR(EIO);
}
return 0;
default:
avpriv_report_missing_feature(ctx, "%s", av_get_pix_fmt_name(fmt));
return AVERROR(EIO);
}
return 0;
}
static int config_input(AVFilterLink *inlink)
{
AVFilterContext *context = inlink->dst;
DnnProcessingContext *ctx = context->priv;
DNNReturnType result;
DNNData model_input;
int check;
result = ff_dnn_get_input(&ctx->dnnctx, &model_input);
if (result != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "could not get input from the model\n");
return AVERROR(EIO);
}
check = check_modelinput_inlink(&model_input, inlink);
if (check != 0) {
return check;
}
return 0;
}
static av_always_inline int isPlanarYUV(enum AVPixelFormat pix_fmt)
{
const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(pix_fmt);
av_assert0(desc);
return !(desc->flags & AV_PIX_FMT_FLAG_RGB) && desc->nb_components == 3;
}
static int prepare_uv_scale(AVFilterLink *outlink)
{
AVFilterContext *context = outlink->src;
DnnProcessingContext *ctx = context->priv;
AVFilterLink *inlink = context->inputs[0];
enum AVPixelFormat fmt = inlink->format;
if (isPlanarYUV(fmt)) {
if (inlink->w != outlink->w || inlink->h != outlink->h) {
if (fmt == AV_PIX_FMT_NV12) {
ctx->sws_uv_scale = sws_getContext(inlink->w >> 1, inlink->h >> 1, AV_PIX_FMT_YA8,
outlink->w >> 1, outlink->h >> 1, AV_PIX_FMT_YA8,
SWS_BICUBIC, NULL, NULL, NULL);
ctx->sws_uv_height = inlink->h >> 1;
} else {
const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(fmt);
int sws_src_h = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
int sws_src_w = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
int sws_dst_h = AV_CEIL_RSHIFT(outlink->h, desc->log2_chroma_h);
int sws_dst_w = AV_CEIL_RSHIFT(outlink->w, desc->log2_chroma_w);
ctx->sws_uv_scale = 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);
ctx->sws_uv_height = sws_src_h;
}
}
}
return 0;
}
static int config_output(AVFilterLink *outlink)
{
AVFilterContext *context = outlink->src;
DnnProcessingContext *ctx = context->priv;
DNNReturnType result;
AVFilterLink *inlink = context->inputs[0];
// have a try run in case that the dnn model resize the frame
result = ff_dnn_get_output(&ctx->dnnctx, inlink->w, inlink->h, &outlink->w, &outlink->h);
if (result != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "could not get output from the model\n");
return AVERROR(EIO);
}
prepare_uv_scale(outlink);
return 0;
}
static int copy_uv_planes(DnnProcessingContext *ctx, AVFrame *out, const AVFrame *in)
{
const AVPixFmtDescriptor *desc;
int uv_height;
if (!ctx->sws_uv_scale) {
av_assert0(in->height == out->height && in->width == out->width);
desc = av_pix_fmt_desc_get(in->format);
uv_height = AV_CEIL_RSHIFT(in->height, desc->log2_chroma_h);
for (int i = 1; i < 3; ++i) {
int bytewidth = av_image_get_linesize(in->format, in->width, i);
if (bytewidth < 0) {
return AVERROR(EINVAL);
}
av_image_copy_plane(out->data[i], out->linesize[i],
in->data[i], in->linesize[i],
bytewidth, uv_height);
}
} else if (in->format == AV_PIX_FMT_NV12) {
sws_scale(ctx->sws_uv_scale, (const uint8_t **)(in->data + 1), in->linesize + 1,
0, ctx->sws_uv_height, out->data + 1, out->linesize + 1);
} else {
sws_scale(ctx->sws_uv_scale, (const uint8_t **)(in->data + 1), in->linesize + 1,
0, ctx->sws_uv_height, out->data + 1, out->linesize + 1);
sws_scale(ctx->sws_uv_scale, (const uint8_t **)(in->data + 2), in->linesize + 2,
0, ctx->sws_uv_height, out->data + 2, out->linesize + 2);
}
return 0;
}
static int flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts)
{
DnnProcessingContext *ctx = outlink->src->priv;
int ret;
DNNAsyncStatusType async_state;
ret = ff_dnn_flush(&ctx->dnnctx);
if (ret != DNN_SUCCESS) {
return -1;
}
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (out_frame) {
if (isPlanarYUV(in_frame->format))
copy_uv_planes(ctx, out_frame, in_frame);
av_frame_free(&in_frame);
ret = ff_filter_frame(outlink, out_frame);
if (ret < 0)
return ret;
if (out_pts)
*out_pts = out_frame->pts + pts;
}
av_usleep(5000);
} while (async_state >= DAST_NOT_READY);
return 0;
}
static int activate(AVFilterContext *filter_ctx)
{
AVFilterLink *inlink = filter_ctx->inputs[0];
AVFilterLink *outlink = filter_ctx->outputs[0];
DnnProcessingContext *ctx = filter_ctx->priv;
AVFrame *in = NULL, *out = NULL;
int64_t pts;
int ret, status;
int got_frame = 0;
int async_state;
FF_FILTER_FORWARD_STATUS_BACK(outlink, inlink);
do {
// drain all input frames
ret = ff_inlink_consume_frame(inlink, &in);
if (ret < 0)
return ret;
if (ret > 0) {
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);
if (ff_dnn_execute_model(&ctx->dnnctx, in, out) != DNN_SUCCESS) {
return AVERROR(EIO);
}
}
} while (ret > 0);
// drain all processed frames
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (out_frame) {
if (isPlanarYUV(in_frame->format))
copy_uv_planes(ctx, out_frame, in_frame);
av_frame_free(&in_frame);
ret = ff_filter_frame(outlink, out_frame);
if (ret < 0)
return ret;
got_frame = 1;
}
} while (async_state == DAST_SUCCESS);
// if frame got, schedule to next filter
if (got_frame)
return 0;
if (ff_inlink_acknowledge_status(inlink, &status, &pts)) {
if (status == AVERROR_EOF) {
int64_t out_pts = pts;
ret = flush_frame(outlink, pts, &out_pts);
ff_outlink_set_status(outlink, status, out_pts);
return ret;
}
}
FF_FILTER_FORWARD_WANTED(outlink, inlink);
return 0;
}
static av_cold void uninit(AVFilterContext *ctx)
{
DnnProcessingContext *context = ctx->priv;
sws_freeContext(context->sws_uv_scale);
ff_dnn_uninit(&context->dnnctx);
}
static const AVFilterPad dnn_processing_inputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.config_props = config_input,
},
};
static const AVFilterPad dnn_processing_outputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.config_props = config_output,
},
};
const 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,
FILTER_INPUTS(dnn_processing_inputs),
FILTER_OUTPUTS(dnn_processing_outputs),
.priv_class = &dnn_processing_class,
.activate = activate,
};