This filter accepts all the dnn networks which do image processing.
Currently, frame with formats rgb24 and bgr24 are supported. Other
formats such as gray and YUV will be supported next. The dnn network
can accept data in float32 or uint8 format. And the dnn network can
change frame size.
The following is a python script to halve the value of the first
channel of the pixel. 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
import imageio
in_img = imageio.imread('in.bmp')
in_img = in_img.astype(np.float32)/255.0
in_data = in_img[np.newaxis, :]
filter_data = np.array([0.5, 0, 0, 0, 1., 0, 0, 0, 1.]).reshape(1,1,3,3).astype(np.float32)
filter = tf.Variable(filter_data)
x = tf.placeholder(tf.float32, shape=[1, None, None, 3], 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())
output = sess.run(y, feed_dict={x: in_data})
graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
tf.train.write_graph(graph_def, '.', 'halve_first_channel.pb', as_text=False)
output = output * 255.0
output = output.astype(np.uint8)
imageio.imsave("out.bmp", np.squeeze(output))
To do the same thing with ffmpeg:
- generate halve_first_channel.pb with the above script
- generate halve_first_channel.model with tools/python/convert.py
- try with following commands
./ffmpeg -i input.jpg -vf dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_out:fmt=rgb24:dnn_backend=native -y out.native.png
./ffmpeg -i input.jpg -vf dnn_processing=model=halve_first_channel.pb:input=dnn_in:output=dnn_out:fmt=rgb24:dnn_backend=tensorflow -y out.tf.png
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
Remove the rain in the input image/video by applying the derain
methods based on convolutional neural networks. Training scripts
as well as scripts for model generation are provided in the
repository at https://github.com/XueweiMeng/derain_filter.git.
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
This is a cuda implementation of yadif, which gives us a way to
do deinterlacing when using the nvdec hwaccel. In that scenario
we don't have access to the nvidia deinterlacer.
Lensfun is a library that applies lens correction to an image using a
database of cameras/lenses (you provide the camera and lens models, and
it uses the corresponding database entry's parameters to apply lens
correction). It is licensed under LGPL3.
The lensfun filter utilizes the lensfun library to apply lens
correction to videos as well as images.
This filter was created out of necessity since I wanted to apply lens
correction to a video and the lenscorrection filter did not work for me.
While this filter requires little info from the user to apply lens
correction, the flaw is that lensfun is intended to be used on indvidual
images. When used on a video, the parameters such as focal length is
constant, so lens correction may fail on videos where the camera's focal
length changes (zooming in or out via zoom lens). To use this filter
correctly on videos where such parameters change, timeline editing may
be used since this filter supports it.
Note that valgrind shows a small memory leak which is not from this
filter but from the lensfun library (memory is allocated when loading
the lensfun database but it somehow isn't deallocated even during
cleanup; it is briefly created in the init function of the filter, and
destroyed before the init function returns). This may have been fixed by
the latest commit in the lensfun repository; the current latest release
of lensfun is almost 3 years ago.
Bi-Linear interpolation is used by default as lanczos interpolation
shows more artifacts in the corrected image in my tests.
The lanczos interpolation is derived from lenstool's implementation of
lanczos interpolation. Lenstool is an app within the lensfun repository
which is licensed under GPL3.
v2 of this patch fixes license notice in libavfilter/vf_lensfun.c
v3 of this patch fixes code style and dependency to gplv3 (thanks to
Paul B Mahol for pointing out the mentioned issues).
v4 of this patch fixes more code style issues that were missed in
v3.
v5 of this patch adds line breaks to some of the documentation in
doc/filters.texi (thanks to Gyan Doshi for pointing out the issue).
v6 of this patch fixes more problems (thanks to Moritz Barsnick for
pointing them out).
v7 of this patch fixes use of sqrt() (changed to sqrtf(); thanks to
Moritz Barsnick for pointing this out). Also should be rebased off of
latest master branch commits at this point.
Signed-off-by: Stephen Seo <seo.disparate@gmail.com>
Guo, Yejun
Paul B Mahol
Vladimir Panteleev
Jarek Samic
Eugene Lyapustin
Mark Thompson
Xuewei Meng
Ruiling Song
Zachary Zhou
Marton Balint
Philip Langdale
Martin Vignali
Roman Arzumanyan
Danil Iashchenko
Mina
Stephen Seo
Sergey Lavrushkin