Allow to set the EOF timestamp.
Also: doc/filters/testsrc*: specify the rounding of the duration option.
The changes in the ref files are right.
For filter-fps-down, the graph is testsrc2=r=7:d=3.5,fps=3.
3.5=24.5/7, so the EOF of testsrc2 will have PTS 25/7.
25/7=(10+5/7)/3, so the EOF PTS for fps should be 11/7,
and the output should contain a frame at PTS 10.
For filter-fps-up, the graph is testsrc2=r=3:d=2,fps=7,
for filter-fps-up-round-down and filter-fps-up-round-up
it is the same with explicit rounding options.
But there is no rounding: testsrc2 produces exactly 6 frames
and 2 seconds, fps converts it into exactly 14 frames.
The tests should probably be adjusted to restore them to
a useful coverage.
Expressions for option fontsize of video filter drawtext have been
supported since commit 6442e4ab3c.
Signed-off-by: Andrei Rybak <rybak.a.v@gmail.com>
Revised-by: Gyan Doshi <ffmpeg@gyani.pro>
This is the only use of 'FontName' with that capitalization, as both
source-code and tests use 'Fontname'. Having consistent capitalization
makes it easier to find the relevant source from the docs.
See these examples for other uses:
libavcodec/ass_split.c:68
tests/ref/fate/sub-cc:9
We can try with the srcnn model from sr filter.
1) get srcnn.pb model file, see filter sr
2) convert srcnn.pb into openvino model with command:
python mo_tf.py --input_model srcnn.pb --data_type=FP32 --input_shape [1,960,1440,1] --keep_shape_ops
See the script at https://github.com/openvinotoolkit/openvino/tree/master/model-optimizer
We'll see srcnn.xml and srcnn.bin at current path, copy them to the
directory where ffmpeg is.
I have also uploaded the model files at https://github.com/guoyejun/dnn_processing/tree/master/models
3) run with openvino backend:
ffmpeg -i input.jpg -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=openvino:model=srcnn.xml:input=x:output=srcnn/Maximum -y srcnn.ov.jpg
(The input.jpg resolution is 720*480)
Also copy the logs on my skylake machine (4 cpus) locally with openvino backend
and tensorflow backend. just for your information.
$ time ./ffmpeg -i 480p.mp4 -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=tensorflow:model=srcnn.pb:input=x:output=y -y srcnn.tf.mp4
…
frame= 343 fps=2.1 q=31.0 Lsize= 2172kB time=00:00:11.76 bitrate=1511.9kbits/s speed=0.0706x
video:1973kB audio:187kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.517637%
[aac @ 0x2f5db80] Qavg: 454.353
real 2m46.781s
user 9m48.590s
sys 0m55.290s
$ time ./ffmpeg -i 480p.mp4 -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=openvino:model=srcnn.xml:input=x:output=srcnn/Maximum -y srcnn.ov.mp4
…
frame= 343 fps=4.0 q=31.0 Lsize= 2172kB time=00:00:11.76 bitrate=1511.9kbits/s speed=0.137x
video:1973kB audio:187kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.517640%
[aac @ 0x31a9040] Qavg: 454.353
real 1m25.882s
user 5m27.004s
sys 0m0.640s
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
Currently, the zoompan filter exposes a 'time' variable (missing from docs) for use in
the 'zoom', 'x', and 'y' expressions. This variable is perhaps better named
'out_time' as it represents the timestamp in seconds of each output frame
produced by zoompan. This patch adds aliases 'out_time' and 'ot' for 'time'.
This patch also adds an 'in_time' (alias 'it') variable that provides access
to the timestamp in seconds of each input frame to the zoompan filter.
This helps to design zoompan filters that depend on the input video timestamps.
For example, it makes it easy to zoom in instantly for only some portion of a video.
Both the 'out_time' and 'in_time' variables have been added in the documentation
for zoompan.
Example usage of 'in_time' in the zoompan filter to zoom in 2x for the
first second of the input video and 1x for the rest:
zoompan=z='if(between(in_time,0,1),2,1):d=1'
V2: Fix zoompan filter documentation stating that the time variable
would be NAN if the input timestamp is unknown.
V3: Add 'it' alias for 'in_time. Add 'out_time' and 'ot' aliases for 'time'.
Minor corrections to zoompan docs.
Signed-off-by: exwm <thighsman@protonmail.com>
Because not every user know about in_pad and out_pad reasonable value range
so maybe try to set 1.0, but setting 1.0 is so hugh to get an fatal error.
Suggested-by: Paul B Mahol <onemda@gmail.com>
Signed-off-by: Steven Liu <lq@chinaffmpeg.org>
currently, the model outputs the rain, and so need a subtraction
in filter c code to get the final derain result.
I've sent a PR to update the model file and accepted, see at
https://github.com/XueweiMeng/derain_filter/pull/3
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Steven Liu <lq@chinaffmpeg.org>
The Y channel is handled by dnn, and also resized by dnn. The UV channels
are resized with swscale.
The command to use espcn.pb (see vf_sr) looks like:
./ffmpeg -i 480p.jpg -vf format=yuv420p,dnn_processing=dnn_backend=tensorflow:model=espcn.pb:input=x:output=y -y tmp.espcn.jpg
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Reviewed-by: Pedro Arthur <bygrandao@gmail.com>
Only the Y channel is handled by dnn, the UV channels are copied
without changes.
The command to use srcnn.pb (see vf_sr) looks like:
./ffmpeg -i 480p.jpg -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=tensorflow:model=srcnn.pb:input=x:output=y -y srcnn.jpg
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Reviewed-by: Pedro Arthur <bygrandao@gmail.com>
As we find ourselves wanting a way to transfer frames between
HW devices (or more realistically, between APIs on the same device),
it's desirable to have a way to describe the relationship. While
we could imagine introducing a `hwtransfer` filter, there is
almost no difference from `hwupload`. The main new feature we need
is a way to specify the target device. Having a single device
for the filter chain is obviously insufficient if we're dealing
with two devices.
So let's add a way to specify the upload target device, and if none
is specified, continue with the existing behaviour.
We must also correctly preserve the sw_format on such a transfer.
There was no consensus about separating AVExprState from AVExpr so here is a
minimal patch using the existing AVExpr to fix ticket #7528.
Signed-off-by: Marton Balint <cus@passwd.hu>
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>
Paul B Mahol
Gyan Doshi
Romane Lafon
Nicolas George
Andrei Rybak
Harry Mallon
tomajsjiang
Kyle Swanson
Joe Ratterman
Guo, Yejun
exwm
Limin Wang
Steven Liu
Lou Logan
Yaroslav Pogrebnyak
Jun Zhao
Philip Langdale
Marton Balint
Michael Niedermayer
Alexandre Heitor Schmidt
Carl Eugen Hoyos