it's stranage to use option "level" in runtime change path but used
"quality" in option, add "quality" in runtime change path, it's more
intuitive and keep the "level" for compatibility.
Reviewe-by: Michael Niedermayer <michael@niedermayer.cc>
Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
libavformat/img2.h: New field export_path_metadata to
VideoDemuxData to only allow the use of the extra metadata
upon explicit user request, for security reasons.
libavformat/img2dec.c: Modify image2 demuxer to make available
two special metadata entries called lavf.image2dec.source_path
and lavf.image2dec.source_basename, which represents, respectively,
the complete path to the source image for the current frame and
the basename i.e. the file name related to the current frame.
These can then be used by filters like drawtext and others. The
metadata fields will only be available when explicitly enabled
with image2 option -export_path_metadata 1.
doc/demuxers.texi: Documented the new metadata fields available
for image2 and how to use them.
doc/filters.texi: Added an example on how to use the new metadata
fields with drawtext filter, in order to plot the input file path
to each output frame.
Usage example:
ffmpeg -f image2 -export_path_metadata 1 -pattern_type glob
-framerate 18 -i '/path/to/input/files/*.jpg'
-filter_complex drawtext="fontsize=40:fontcolor=white:
fontfile=FreeSans.ttf:borderw=2:bordercolor=black:
text='%{metadata\:lavf.image2dec.source_basename\:NA}':x=5:y=50"
output.avi
Fixes#2874.
Signed-off-by: Alexandre Heitor Schmidt <alexandre.schmidt@gmail.com>
Signed-off-by: Marton Balint <cus@passwd.hu>
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>
do not request AVFrame's format in vf_ddn_processing with 'fmt',
but to add another filter for the format.
command examples:
./ffmpeg -i input.jpg -vf format=bgr24,dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_out:dnn_backend=native -y out.native.png
./ffmpeg -i input.jpg -vf format=rgb24,dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_out:dnn_backend=native -y out.native.png
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
In order to access the original opaque parameter of a buffer in the buffer
pool. (The buffer pool implementation overrides the normal opaque parameter but
also saves it so it is accessible).
v2: add assertion check before dereferencing the BufferPoolEntry.
Signed-off-by: Marton Balint <cus@passwd.hu>
ts_target_bitrate is in kbps, not bps. This commit clarifies the unit
and modifies the example to match the description.
Signed-off-by: James Zern <jzern@google.com>
It performs HDR(High Dynamic Range) to SDR(Standard Dynamic Range) conversion
with tone-mapping. It only supports HDR10 as input temporarily.
An example command to use this filter with vaapi codecs:
FFMPEG -hwaccel vaapi -vaapi_device /dev/dri/renderD128 -hwaccel_output_format vaapi \
-i INPUT -vf 'tonemap_vaapi=format=p010' -c:v hevc_vaapi -profile 2 OUTPUT
Signed-off-by: Xinpeng Sun <xinpeng.sun@intel.com>
Signed-off-by: Zachary Zhou <zachary.zhou@intel.com>
Signed-off-by: Ruiling Song <ruiling.song@intel.com>
add linger parameter to libsrt, it's setting the number of seconds
that the socket waits for unsent data when closing.
Reviewed-by: Andriy Gelman <andriy.gelman@gmail.com>
Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
Adjustment of evaluated values shifted to ff_adjust_scale_dimensions
Shifted code for force_original_aspect_ratio and force_divisble_by from
vf_scale so it is now available for scale_cuda, scale_npp and
scale_vaapi as well.
This sets the range of the first automatically assigned PMT PID or elementary
stream PID parameters to [0x20, 0x1ffa]. You can still assign manually a PID
for a stream using AVStream->id in the wider [0x10, 0x1ffe] range as specified
by ISO13818-1. But since DVB and ATSC both reserves some PIDs, let's not allow
them to be automatically assigned.
Also make sure that assigned PID numbers are valid and fix the error message
for the previous PID collision checks.
Signed-off-by: Marton Balint <cus@passwd.hu>
Disable by default to output all the layers, to match libaomdec wrapper.
Add option to select the operating point for the spatial layers.
Update the documentation with the new options.
Signed-off-by: James Almer <jamrial@gmail.com>
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>
This introduces two new AVOption options for the FTP protocol,
one named ftp-user to supply the username to be used for auth,
one named ftp-password to supply the password to be used for auth.
These are useful for when an API user does not wish to deal with
URL manipulation and percent encoding.
Setting them while also having credentials in the URL will use the
credentials from the URL. The rationale for this is that credentials
embedded in the URL are probably more specific to what the user is
trying to do than anything set by some API user.
Signed-off-by: Nicolas Frattaroli <ffmpeg@fratti.ch>
Signed-off-by: Marton Balint <cus@passwd.hu>
Allows user to set maximum number of buffered packets when
probing a codec. It was a hard-coded parameter before this commit.
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
