summaryrefslogtreecommitdiffstats
diff options
context:
space:
mode:
Diffstat
-rwxr-xr-xconfigure1
-rw-r--r--doc/filters.texi44
-rw-r--r--libavfilter/Makefile1
-rw-r--r--libavfilter/allfilters.c1
-rw-r--r--libavfilter/vf_dnn_processing.c331
5 files changed, 378 insertions, 0 deletions
diff --git a/configure b/configure
index f0be66e..0dcd234 100755
--- a/configure
+++ b/configure
@@ -3473,6 +3473,7 @@ derain_filter_select="dnn"
deshake_filter_select="pixelutils"
deshake_opencl_filter_deps="opencl"
dilation_opencl_filter_deps="opencl"
+dnn_processing_filter_select="dnn"
drawtext_filter_deps="libfreetype"
drawtext_filter_suggest="libfontconfig libfribidi"
elbg_filter_deps="avcodec"
diff --git a/doc/filters.texi b/doc/filters.texi
index 6d893d8..6800124 100644
--- a/doc/filters.texi
+++ b/doc/filters.texi
@@ -8928,6 +8928,50 @@ ffmpeg -i INPUT -f lavfi -i nullsrc=hd720,geq='r=128+80*(sin(sqrt((X-W/2)*(X-W/2
@end example
@end itemize
+@section dnn_processing
+
+Do image processing with deep neural networks. Currently only AVFrame with RGB24
+and BGR24 are supported, more formats will be added later.
+
+The filter accepts the following options:
+
+@table @option
+@item dnn_backend
+Specify which DNN backend to use for model loading and execution. This option accepts
+the following values:
+
+@table @samp
+@item native
+Native implementation of DNN loading and execution.
+
+@item tensorflow
+TensorFlow backend. To enable this backend you
+need to install the TensorFlow for C library (see
+@url{https://www.tensorflow.org/install/install_c}) and configure FFmpeg with
+@code{--enable-libtensorflow}
+@end table
+
+Default value is @samp{native}.
+
+@item model
+Set path to model file specifying network architecture and its parameters.
+Note that different backends use different file formats. TensorFlow and native
+backend can load files for only its format.
+
+Native model file (.model) can be generated from TensorFlow model file (.pb) by using tools/python/convert.py
+
+@item input
+Set the input name of the dnn network.
+
+@item output
+Set the output name of the dnn network.
+
+@item fmt
+Set the pixel format for the Frame. Allowed values are @code{AV_PIX_FMT_RGB24}, and @code{AV_PIX_FMT_BGR24}.
+Default value is @code{AV_PIX_FMT_RGB24}.
+
+@end table
+
@section drawbox
Draw a colored box on the input image.
diff --git a/libavfilter/Makefile b/libavfilter/Makefile
index 2080eed..3eff398 100644
--- a/libavfilter/Makefile
+++ b/libavfilter/Makefile
@@ -223,6 +223,7 @@ OBJS-$(CONFIG_DILATION_FILTER) += vf_neighbor.o
OBJS-$(CONFIG_DILATION_OPENCL_FILTER) += vf_neighbor_opencl.o opencl.o \
opencl/neighbor.o
OBJS-$(CONFIG_DISPLACE_FILTER) += vf_displace.o framesync.o
+OBJS-$(CONFIG_DNN_PROCESSING_FILTER) += vf_dnn_processing.o
OBJS-$(CONFIG_DOUBLEWEAVE_FILTER) += vf_weave.o
OBJS-$(CONFIG_DRAWBOX_FILTER) += vf_drawbox.o
OBJS-$(CONFIG_DRAWGRAPH_FILTER) += f_drawgraph.o
diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c
index cb60906..7c1e19e 100644
--- a/libavfilter/allfilters.c
+++ b/libavfilter/allfilters.c
@@ -209,6 +209,7 @@ extern AVFilter ff_vf_detelecine;
extern AVFilter ff_vf_dilation;
extern AVFilter ff_vf_dilation_opencl;
extern AVFilter ff_vf_displace;
+extern AVFilter ff_vf_dnn_processing;
extern AVFilter ff_vf_doubleweave;
extern AVFilter ff_vf_drawbox;
extern AVFilter ff_vf_drawgraph;
diff --git a/libavfilter/vf_dnn_processing.c b/libavfilter/vf_dnn_processing.c
new file mode 100644
index 0000000..87ad354
--- /dev/null
+++ b/libavfilter/vf_dnn_processing.c
@@ -0,0 +1,331 @@
+/*
+ * 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 "avfilter.h"
+#include "dnn_interface.h"
+#include "formats.h"
+#include "internal.h"
+
+typedef struct DnnProcessingContext {
+ const AVClass *class;
+
+ char *model_filename;
+ DNNBackendType backend_type;
+ enum AVPixelFormat fmt;
+ char *model_inputname;
+ char *model_outputname;
+
+ DNNModule *dnn_module;
+ DNNModel *model;
+
+ // input & output of the model at execution time
+ DNNData input;
+ DNNData output;
+} DnnProcessingContext;
+
+#define OFFSET(x) offsetof(DnnProcessingContext, 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 }, 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
+ { "model", "path to model file", OFFSET(model_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
+ { "input", "input name of the model", OFFSET(model_inputname), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
+ { "output", "output name of the model", OFFSET(model_outputname), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
+ { "fmt", "AVPixelFormat of the frame", OFFSET(fmt), AV_OPT_TYPE_PIXEL_FMT, { .i64=AV_PIX_FMT_RGB24 }, AV_PIX_FMT_NONE, AV_PIX_FMT_NB - 1, FLAGS },
+ { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(dnn_processing);
+
+static av_cold int init(AVFilterContext *context)
+{
+ DnnProcessingContext *ctx = context->priv;
+ int supported = 0;
+ // as the first step, only rgb24 and bgr24 are supported
+ const enum AVPixelFormat supported_pixel_fmts[] = {
+ AV_PIX_FMT_RGB24,
+ AV_PIX_FMT_BGR24,
+ };
+ for (int i = 0; i < sizeof(supported_pixel_fmts) / sizeof(enum AVPixelFormat); ++i) {
+ if (supported_pixel_fmts[i] == ctx->fmt) {
+ supported = 1;
+ break;
+ }
+ }
+ if (!supported) {
+ av_log(context, AV_LOG_ERROR, "pixel fmt %s not supported yet\n",
+ av_get_pix_fmt_name(ctx->fmt));
+ return AVERROR(AVERROR_INVALIDDATA);
+ }
+
+ if (!ctx->model_filename) {
+ av_log(ctx, AV_LOG_ERROR, "model file for network is not specified\n");
+ return AVERROR(EINVAL);
+ }
+ if (!ctx->model_inputname) {
+ av_log(ctx, AV_LOG_ERROR, "input name of the model network is not specified\n");
+ return AVERROR(EINVAL);
+ }
+ if (!ctx->model_outputname) {
+ av_log(ctx, AV_LOG_ERROR, "output name of the model network is not specified\n");
+ return AVERROR(EINVAL);
+ }
+
+ ctx->dnn_module = ff_get_dnn_module(ctx->backend_type);
+ if (!ctx->dnn_module) {
+ av_log(ctx, AV_LOG_ERROR, "could not create DNN module for requested backend\n");
+ return AVERROR(ENOMEM);
+ }
+ if (!ctx->dnn_module->load_model) {
+ av_log(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);
+ if (!ctx->model) {
+ av_log(ctx, AV_LOG_ERROR, "could not load DNN model\n");
+ return AVERROR(EINVAL);
+ }
+
+ return 0;
+}
+
+static int query_formats(AVFilterContext *context)
+{
+ AVFilterFormats *formats;
+ DnnProcessingContext *ctx = context->priv;
+ enum AVPixelFormat pixel_fmts[2];
+ pixel_fmts[0] = ctx->fmt;
+ pixel_fmts[1] = AV_PIX_FMT_NONE;
+
+ formats = ff_make_format_list(pixel_fmts);
+ return ff_set_common_formats(context, formats);
+}
+
+static int config_input(AVFilterLink *inlink)
+{
+ AVFilterContext *context = inlink->dst;
+ DnnProcessingContext *ctx = context->priv;
+ DNNReturnType result;
+ DNNData dnn_data;
+
+ result = ctx->model->get_input(ctx->model->model, &dnn_data, ctx->model_inputname);
+ if (result != DNN_SUCCESS) {
+ av_log(ctx, AV_LOG_ERROR, "could not get input from the model\n");
+ return AVERROR(EIO);
+ }
+
+ // the design is to add explicit scale filter before this filter
+ if (dnn_data.height != -1 && dnn_data.height != inlink->h) {
+ av_log(ctx, AV_LOG_ERROR, "the model requires frame height %d but got %d\n",
+ dnn_data.height, inlink->h);
+ return AVERROR(EIO);
+ }
+ if (dnn_data.width != -1 && dnn_data.width != inlink->w) {
+ av_log(ctx, AV_LOG_ERROR, "the model requires frame width %d but got %d\n",
+ dnn_data.width, inlink->w);
+ return AVERROR(EIO);
+ }
+
+ if (dnn_data.channels != 3) {
+ av_log(ctx, AV_LOG_ERROR, "the model requires input channels %d\n",
+ dnn_data.channels);
+ return AVERROR(EIO);
+ }
+ if (dnn_data.dt != DNN_FLOAT && dnn_data.dt != DNN_UINT8) {
+ av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type as float32 and uint8.\n");
+ return AVERROR(EIO);
+ }
+
+ ctx->input.width = inlink->w;
+ ctx->input.height = inlink->h;
+ ctx->input.channels = dnn_data.channels;
+ ctx->input.dt = dnn_data.dt;
+
+ result = (ctx->model->set_input_output)(ctx->model->model,
+ &ctx->input, ctx->model_inputname,
+ (const char **)&ctx->model_outputname, 1);
+ if (result != DNN_SUCCESS) {
+ av_log(ctx, AV_LOG_ERROR, "could not set input and output for the model\n");
+ return AVERROR(EIO);
+ }
+
+ return 0;
+}
+
+static int config_output(AVFilterLink *outlink)
+{
+ AVFilterContext *context = outlink->src;
+ DnnProcessingContext *ctx = context->priv;
+ DNNReturnType result;
+
+ // have a try run in case that the dnn model resize the frame
+ result = (ctx->dnn_module->execute_model)(ctx->model, &ctx->output, 1);
+ if (result != DNN_SUCCESS){
+ av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
+ return AVERROR(EIO);
+ }
+
+ outlink->w = ctx->output.width;
+ outlink->h = ctx->output.height;
+
+ return 0;
+}
+
+static int copy_from_frame_to_dnn(DNNData *dnn_data, const AVFrame *in)
+{
+ // extend this function to support more formats
+ av_assert0(in->format == AV_PIX_FMT_RGB24 || in->format == AV_PIX_FMT_RGB24);
+
+ if (dnn_data->dt == DNN_FLOAT) {
+ float *dnn_input = dnn_data->data;
+ for (int i = 0; i < in->height; i++) {
+ for(int j = 0; j < in->width * 3; j++) {
+ int k = i * in->linesize[0] + j;
+ int t = i * in->width * 3 + j;
+ dnn_input[t] = in->data[0][k] / 255.0f;
+ }
+ }
+ } else {
+ uint8_t *dnn_input = dnn_data->data;
+ av_assert0(dnn_data->dt == DNN_UINT8);
+ for (int i = 0; i < in->height; i++) {
+ for(int j = 0; j < in->width * 3; j++) {
+ int k = i * in->linesize[0] + j;
+ int t = i * in->width * 3 + j;
+ dnn_input[t] = in->data[0][k];
+ }
+ }
+ }
+
+ return 0;
+}
+
+static int copy_from_dnn_to_frame(AVFrame *out, const DNNData *dnn_data)
+{
+ // extend this function to support more formats
+ av_assert0(out->format == AV_PIX_FMT_RGB24 || out->format == AV_PIX_FMT_RGB24);
+
+ if (dnn_data->dt == DNN_FLOAT) {
+ float *dnn_output = dnn_data->data;
+ for (int i = 0; i < out->height; i++) {
+ for(int j = 0; j < out->width * 3; j++) {
+ int k = i * out->linesize[0] + j;
+ int t = i * out->width * 3 + j;
+ out->data[0][k] = av_clip((int)(dnn_output[t] * 255.0f), 0, 255);
+ }
+ }
+ } else {
+ uint8_t *dnn_output = dnn_data->data;
+ av_assert0(dnn_data->dt == DNN_UINT8);
+ for (int i = 0; i < out->height; i++) {
+ for(int j = 0; j < out->width * 3; j++) {
+ int k = i * out->linesize[0] + j;
+ int t = i * out->width * 3 + j;
+ out->data[0][k] = dnn_output[t];
+ }
+ }
+ }
+
+ 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->input, 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);
+ }
+ av_assert0(ctx->output.channels == 3);
+
+ 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(out, &ctx->output);
+ av_frame_free(&in);
+ return ff_filter_frame(outlink, out);
+}
+
+static av_cold void uninit(AVFilterContext *ctx)
+{
+ DnnProcessingContext *context = ctx->priv;
+
+ 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,
+};
OpenPOWER on IntegriCloud