This commit adds a blend_vulkan filter and a normal blend mode, and
reserves support for introducing the blend modes in the future.
Use the commands below to test: (href: https://trac.ffmpeg.org/wiki/Blend)
I. make an image for test
ffmpeg -f lavfi -i color=s=256x256,geq=r='H-1-Y':g='H-1-Y':b='H-1-Y' -frames 1 \
-y -pix_fmt yuv420p test.jpg
II. blend in sw
ffmpeg -i test.jpg -vf "split[a][b];[b]transpose[b];[a][b]blend=all_mode=normal,\
pseudocolor=preset=turbo" -y normal_sw.jpg
III. blend in vulkan
ffmpeg -init_hw_device vulkan -i test.jpg -vf "split[a][b];[b]transpose[b];\
[a]hwupload[a];[b]hwupload[b];[a][b]blend_vulkan=all_mode=normal,hwdownload,\
format=yuv420p,pseudocolor=preset=turbo" -y normal_vulkan.jpg
Signed-off-by: Wu Jianhua <jianhua.wu@intel.com>
This allows to remove the spurious dependencies of mpegvideo encoders
on error_resilience; some other components that do not use mpegvideo
to its fullest turned out to not need it either.
Adding a new CONFIG_EXTRA needs a reconfigure to take effect.
In order to force this a few unnecessary headers from lavfi/allfilters.c
have been removed.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
deinterlaces CVPixelBuffers, i.e. AV_PIX_FMT_VIDEOTOOLBOX frames
for example, an interlaced mpeg2 video can be decoded by avcodec,
uploaded into a CVPixelBuffer, deinterlaced by Metal, and then
encoded to h264 by VideoToolbox as follows:
ffmpeg \
-init_hw_device videotoolbox \
-i interlaced.ts \
-vf hwupload,yadif_videotoolbox \
-c:v h264_videotoolbox \
-b:v 2000k \
-c:a copy \
-y progressive.ts
(note that uploading AVFrame into CVPixelBuffer via hwupload
requires 504c60660d)
this work is sponsored by Fancy Bits LLC
Reviewed-by: Ridley Combs <rcombs@rcombs.me>
Reviewed-by: Philip Langdale <philipl@overt.org>
Signed-off-by: Aman Karmani <aman@tmm1.net>
The following command is on how to apply transpose_vulkan filter:
ffmpeg -init_hw_device vulkan -i input.264 -vf \
hwupload=extra_hw_frames=16,transpose_vulkan,hwdownload,format=yuv420p output.264
Signed-off-by: Wu Jianhua <jianhua.wu@intel.com>
This filter flips the input video both horizontally and vertically
in one compute pipeline, and it's no need to use two pipelines for
hflip_vulkan,vflip_vulkan anymore.
Signed-off-by: Wu Jianhua <jianhua.wu@intel.com>
The following command is on how to apply vflip_vulkan filter:
ffmpeg -init_hw_device vulkan -i input.264 -vf hwupload=extra_hw_frames=16,vflip_vulkan,hwdownload,format=yuv420p output.264
Signed-off-by: Wu Jianhua <jianhua.wu@intel.com>
The following command is on how to apply hflip_vulkan filter:
ffmpeg -init_hw_device vulkan -i input.264 -vf hwupload=extra_hw_frames=16,hflip_vulkan,hwdownload,format=yuv420p output.264
Signed-off-by: Wu Jianhua <jianhua.wu@intel.com>
This commit adds a powerful and customizable gblur Vulkan filter,
which provides a maximum 127x127 kernel size of Gaussian Filter.
The size could be adjusted by requirements on quality or performance.
The following command is on how to apply gblur_vulkan filter:
ffmpeg -init_hw_device vulkan -i input.264 -vf hwupload=extra_hw_frames=16,gblur_vulkan,hwdownload,format=yuv420p output.264
Signed-off-by: Wu Jianhua <jianhua.wu@intel.com>
This filter conceptually maps the libplacebo `pl_renderer` API into
libavfilter, which is a high-level image rendering API designed to work
with an RGB pipeline internally. As such, there's no way to avoid e.g.
chroma interpolation with this filter, although new versions of
libplacebo support outputting back to subsampled YCbCr after processing
is done.
That being said, `pl_renderer` supports automatic integration of the
majority of libplacebo's shaders, ranging from debanding to tone
mapping, and also supports loading custom mpv-style user shaders, making
this API a natural candidate for getting a lot of functionality out of
relatively little code.
In the future, I may approach this problem either by rewriting this
filter to also support a non-renderer codepath, or by upgrading
libplacebo's renderer to support a full YCbCr pipeline.
This unfortunately requires a very new version of libplacebo (unreleased
at time of writing) for timeline semaphore support. But the amount of
boilerplate needed to hack in backwards compatibility would have been
very unreasonable.
Implements a gray world color correction algorithm
using a log scale LAB colorspace.
Signed-off-by: Paul Buxton <paulbuxton.mail@googlemail.com>
Signed-off-by: Paul B Mahol <onemda@gmail.com>
Add examples on how to use this filter, and improve the code style.
Implement the slice-level parallelism for guided filter.
Add the basic version of guided filter.
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
Reviewed-by: Steven Liu <liuqi05@kuaishou.com>
classification is done on every detection bounding box in frame's side data,
which are the results of object detection (filter dnn_detect).
Please refer to commit log of dnn_detect for the material for detection,
and see below for classification.
- download material for classifcation:
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.bin
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.xml
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.label
- run command as:
./ffmpeg -i cici.jpg -vf dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:input=data:output=detection_out:confidence=0.6:labels=face-detection-adas-0001.label,dnn_classify=dnn_backend=openvino:model=emotions-recognition-retail-0003.xml:input=data:output=prob_emotion:confidence=0.3:labels=emotions-recognition-retail-0003.label:target=face,showinfo -f null -
We'll see the detect&classify result as below:
[Parsed_showinfo_2 @ 0x55b7d25e77c0] side data - detection bounding boxes:
[Parsed_showinfo_2 @ 0x55b7d25e77c0] source: face-detection-adas-0001.xml, emotions-recognition-retail-0003.xml
[Parsed_showinfo_2 @ 0x55b7d25e77c0] index: 0, region: (1005, 813) -> (1086, 905), label: face, confidence: 10000/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] classify: label: happy, confidence: 6757/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] index: 1, region: (888, 839) -> (967, 926), label: face, confidence: 6917/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] classify: label: anger, confidence: 4320/10000.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
This is possible now that the next-API is gone.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
Signed-off-by: James Almer <jamrial@gmail.com>
Deprecated in c29038f304.
The resample filter based upon this library has been removed as well.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
Signed-off-by: James Almer <jamrial@gmail.com>
Below are the example steps to do object detection:
1. download and install l_openvino_toolkit_p_2021.1.110.tgz from
https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/download.html
or, we can get source code (tag 2021.1), build and install.
2. export LD_LIBRARY_PATH with openvino settings, for example:
.../deployment_tools/inference_engine/lib/intel64/:.../deployment_tools/inference_engine/external/tbb/lib/
3. rebuild ffmpeg from source code with configure option:
--enable-libopenvino
--extra-cflags='-I.../deployment_tools/inference_engine/include/'
--extra-ldflags='-L.../deployment_tools/inference_engine/lib/intel64'
4. download model files and test image
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/face-detection-adas-0001.bin
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/face-detection-adas-0001.xml
wget
https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/face-detection-adas-0001.label
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/images/cici.jpg
5. run ffmpeg with:
./ffmpeg -i cici.jpg -vf dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:input=data:output=detection_out:confidence=0.6:labels=face-detection-adas-0001.label,showinfo -f null -
We'll see the detect result as below:
[Parsed_showinfo_1 @ 0x560c21ecbe40] side data - detection bounding boxes:
[Parsed_showinfo_1 @ 0x560c21ecbe40] source: face-detection-adas-0001.xml
[Parsed_showinfo_1 @ 0x560c21ecbe40] index: 0, region: (1005, 813) -> (1086, 905), label: face, confidence: 10000/10000.
[Parsed_showinfo_1 @ 0x560c21ecbe40] index: 1, region: (888, 839) -> (967, 926), label: face, confidence: 6917/10000.
There are two faces detected with confidence 100% and 69.17%.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
This is Visual Information Fidelity (VIF) filter and one of the component
filters of VMAF. It outputs the average VIF score over all frames.
Signed-off-by: Ashish Singh <ashk43712@gmail.com>
