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>
