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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> |
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compat | ||
doc | ||
ffbuild | ||
fftools | ||
libavcodec | ||
libavdevice | ||
libavfilter | ||
libavformat | ||
libavresample | ||
libavutil | ||
libpostproc | ||
libswresample | ||
libswscale | ||
presets | ||
tests | ||
tools | ||
.gitattributes | ||
.gitignore | ||
.travis.yml | ||
Changelog | ||
configure | ||
CONTRIBUTING.md | ||
COPYING.GPLv2 | ||
COPYING.GPLv3 | ||
COPYING.LGPLv2.1 | ||
COPYING.LGPLv3 | ||
CREDITS | ||
INSTALL.md | ||
LICENSE.md | ||
MAINTAINERS | ||
Makefile | ||
README.md | ||
RELEASE |
FFmpeg README
FFmpeg is a collection of libraries and tools to process multimedia content such as audio, video, subtitles and related metadata.
Libraries
libavcodec
provides implementation of a wider range of codecs.libavformat
implements streaming protocols, container formats and basic I/O access.libavutil
includes hashers, decompressors and miscellaneous utility functions.libavfilter
provides a mean to alter decoded Audio and Video through chain of filters.libavdevice
provides an abstraction to access capture and playback devices.libswresample
implements audio mixing and resampling routines.libswscale
implements color conversion and scaling routines.
Tools
- ffmpeg is a command line toolbox to manipulate, convert and stream multimedia content.
- ffplay is a minimalistic multimedia player.
- ffprobe is a simple analysis tool to inspect multimedia content.
- Additional small tools such as
aviocat
,ismindex
andqt-faststart
.
Documentation
The offline documentation is available in the doc/ directory.
The online documentation is available in the main website and in the wiki.
Examples
Coding examples are available in the doc/examples directory.
License
FFmpeg codebase is mainly LGPL-licensed with optional components licensed under GPL. Please refer to the LICENSE file for detailed information.
Contributing
Patches should be submitted to the ffmpeg-devel mailing list using
git format-patch
or git send-email
. Github pull requests should be
avoided because they are not part of our review process and will be ignored.