37d24a6c8f
The following is a python script to halve the value of the gray
image. It demos how to setup and execute dnn model with python+tensorflow.
It also generates .pb file which will be used by ffmpeg.
import tensorflow as tf
import numpy as np
from skimage import color
from skimage import io
in_img = io.imread('input.jpg')
in_img = color.rgb2gray(in_img)
io.imsave('ori_gray.jpg', np.squeeze(in_img))
in_data = np.expand_dims(in_img, axis=0)
in_data = np.expand_dims(in_data, axis=3)
filter_data = np.array([0.5]).reshape(1,1,1,1).astype(np.float32)
filter = tf.Variable(filter_data)
x = tf.placeholder(tf.float32, shape=[1, None, None, 1], name='dnn_in')
y = tf.nn.conv2d(x, filter, strides=[1, 1, 1, 1], padding='VALID', name='dnn_out')
sess=tf.Session()
sess.run(tf.global_variables_initializer())
graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
tf.train.write_graph(graph_def, '.', 'halve_gray_float.pb', as_text=False)
print("halve_gray_float.pb generated, please use \
path_to_ffmpeg/tools/python/convert.py to generate halve_gray_float.model\n")
output = sess.run(y, feed_dict={x: in_data})
output = output * 255.0
output = output.astype(np.uint8)
io.imsave("out.jpg", np.squeeze(output))
To do the same thing with ffmpeg:
- generate halve_gray_float.pb with the above script
- generate halve_gray_float.model with tools/python/convert.py
- try with following commands
./ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.model:input=dnn_in:output=dnn_out:dnn_backend=native out.native.png
./ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow out.tf.png
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
419 lines
14 KiB
C
419 lines
14 KiB
C
/*
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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 "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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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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} 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_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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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 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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return 0;
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}
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static int copy_from_frame_to_dnn(DNNData *dnn_input, const AVFrame *frame)
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{
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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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float *dnn_input_data = dnn_input->data;
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for (int i = 0; i < frame->height; i++) {
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for(int j = 0; j < frame->width * 3; j++) {
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int k = i * frame->linesize[0] + j;
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int t = i * frame->width * 3 + j;
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dnn_input_data[t] = frame->data[0][k] / 255.0f;
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}
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}
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} else {
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uint8_t *dnn_input_data = dnn_input->data;
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av_assert0(dnn_input->dt == DNN_UINT8);
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for (int i = 0; i < frame->height; i++) {
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for(int j = 0; j < frame->width * 3; j++) {
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int k = i * frame->linesize[0] + j;
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int t = i * frame->width * 3 + j;
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dnn_input_data[t] = frame->data[0][k];
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}
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}
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}
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return 0;
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case AV_PIX_FMT_GRAY8:
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{
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uint8_t *dnn_input_data = dnn_input->data;
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av_assert0(dnn_input->dt == DNN_UINT8);
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for (int i = 0; i < frame->height; i++) {
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for(int j = 0; j < frame->width; j++) {
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int k = i * frame->linesize[0] + j;
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int t = i * frame->width + j;
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dnn_input_data[t] = frame->data[0][k];
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}
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}
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}
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return 0;
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case AV_PIX_FMT_GRAYF32:
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{
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float *dnn_input_data = dnn_input->data;
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av_assert0(dnn_input->dt == DNN_FLOAT);
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for (int i = 0; i < frame->height; i++) {
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for(int j = 0; j < frame->width; j++) {
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int k = i * frame->linesize[0] + j * sizeof(float);
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int t = i * frame->width + j;
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dnn_input_data[t] = *(float*)(frame->data[0] + k);
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}
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}
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}
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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(AVFrame *frame, const DNNData *dnn_output)
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{
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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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float *dnn_output_data = dnn_output->data;
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for (int i = 0; i < frame->height; i++) {
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for(int j = 0; j < frame->width * 3; j++) {
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int k = i * frame->linesize[0] + j;
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int t = i * frame->width * 3 + j;
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frame->data[0][k] = av_clip_uintp2((int)(dnn_output_data[t] * 255.0f), 8);
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}
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}
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} else {
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uint8_t *dnn_output_data = dnn_output->data;
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av_assert0(dnn_output->dt == DNN_UINT8);
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for (int i = 0; i < frame->height; i++) {
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for(int j = 0; j < frame->width * 3; j++) {
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int k = i * frame->linesize[0] + j;
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int t = i * frame->width * 3 + j;
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frame->data[0][k] = dnn_output_data[t];
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}
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}
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}
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return 0;
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case AV_PIX_FMT_GRAY8:
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{
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uint8_t *dnn_output_data = dnn_output->data;
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av_assert0(dnn_output->dt == DNN_UINT8);
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for (int i = 0; i < frame->height; i++) {
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for(int j = 0; j < frame->width; j++) {
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int k = i * frame->linesize[0] + j;
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int t = i * frame->width + j;
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frame->data[0][k] = dnn_output_data[t];
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}
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}
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}
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return 0;
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case AV_PIX_FMT_GRAYF32:
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{
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float *dnn_output_data = dnn_output->data;
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av_assert0(dnn_output->dt == DNN_FLOAT);
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for (int i = 0; i < frame->height; i++) {
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for(int j = 0; j < frame->width; j++) {
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int k = i * frame->linesize[0] + j * sizeof(float);
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int t = i * frame->width + j;
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*(float*)(frame->data[0] + k) = dnn_output_data[t];
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}
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}
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}
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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 filter_frame(AVFilterLink *inlink, AVFrame *in)
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{
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AVFilterContext *context = inlink->dst;
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AVFilterLink *outlink = context->outputs[0];
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DnnProcessingContext *ctx = context->priv;
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DNNReturnType dnn_result;
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AVFrame *out;
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copy_from_frame_to_dnn(&ctx->input, in);
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dnn_result = (ctx->dnn_module->execute_model)(ctx->model, &ctx->output, 1);
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if (dnn_result != DNN_SUCCESS){
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av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
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av_frame_free(&in);
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return AVERROR(EIO);
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}
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out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
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if (!out) {
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av_frame_free(&in);
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return AVERROR(ENOMEM);
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}
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av_frame_copy_props(out, in);
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copy_from_dnn_to_frame(out, &ctx->output);
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av_frame_free(&in);
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return ff_filter_frame(outlink, out);
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}
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static av_cold void uninit(AVFilterContext *ctx)
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{
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DnnProcessingContext *context = ctx->priv;
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if (context->dnn_module)
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(context->dnn_module->free_model)(&context->model);
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av_freep(&context->dnn_module);
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}
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static const AVFilterPad dnn_processing_inputs[] = {
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{
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.name = "default",
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.type = AVMEDIA_TYPE_VIDEO,
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.config_props = config_input,
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.filter_frame = filter_frame,
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},
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{ NULL }
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};
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static const AVFilterPad dnn_processing_outputs[] = {
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{
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.name = "default",
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.type = AVMEDIA_TYPE_VIDEO,
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.config_props = config_output,
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},
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{ NULL }
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};
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AVFilter ff_vf_dnn_processing = {
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.name = "dnn_processing",
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.description = NULL_IF_CONFIG_SMALL("Apply DNN processing filter to the input."),
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.priv_size = sizeof(DnnProcessingContext),
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.init = init,
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.uninit = uninit,
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.query_formats = query_formats,
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.inputs = dnn_processing_inputs,
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.outputs = dnn_processing_outputs,
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.priv_class = &dnn_processing_class,
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};
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