Calculate Spatial Info (SI) and Temporal Info (TI) scores for a video, as defined
in ITU-T P.910: Subjective video quality assessment methods for multimedia
applications.
Overlay one video on the top of another.
It takes two inputs and has one output. The first input is the "main" video on
which the second input is overlaid. This filter requires same memory layout for
all the inputs.
An example command to use this filter to overlay overlay.mp4 at the top-left
corner of the main.mp4:
ffmpeg -init_hw_device vaapi=foo:/dev/dri/renderD128 \
-hwaccel vaapi -hwaccel_device foo -hwaccel_output_format vaapi -c:v h264 -i main.mp4 \
-hwaccel vaapi -hwaccel_device foo -hwaccel_output_format vaapi -c:v h264 -i overlay.mp4 \
-filter_complex "[0:v][1:v]overlay_vaapi=0:0:100💯0.5[t1]" \
-map "[t1]" -an -c:v h264_vaapi -y out_vaapi.mp4
Signed-off-by: U. Artie Eoff <ullysses.a.eoff@intel.com>
Signed-off-by: Xinpeng Sun <xinpeng.sun@intel.com>
Signed-off-by: Zachary Zhou <zachary.zhou@intel.com>
Signed-off-by: Fei Wang <fei.w.wang@intel.com>
Signed-off-by: Haihao Xiang <haihao.xiang@intel.com>
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>
In case of shared builds, some object files containing tables
are currently duplicated into other libraries: log2_tab.c,
golomb.c, reverse.c. The check for whether this is duplicated
is simply whether CONFIG_SHARED is true. Yet this is crude:
E.g. libavdevice includes reverse.c for shared builds, but only
needs it for the decklink input device, which given that decklink
is not enabled by default will be unused in most libavdevice.so.
This commit changes this by making it more explicit about what
to duplicate from other libraries. To do this, two new Makefile
variables were added: SHLIBOBJS and STLIBOBJS. SHLIBOBJS contains
the objects that are duplicated from other libraries in case of
shared builds; STLIBOBJS contains stuff that a library has to
provide for other libraries in case of static builds. These new
variables provide a way to enable/disable with a finer granularity
than just whether shared builds are enabled or not. E.g. lavd's
Makefile now contains: SHLIBOBJS-$(CONFIG_DECKLINK_INDEV) += reverse.o
Another example is provided by the golomb tables. These are provided
by lavc for static builds, even if one uses a build configuration
that makes only lavf use them. Therefore lavc's Makefile contains
STLIBOBJS-$(CONFIG_MXF_MUXER) += golomb.o, whereas lavf's Makefile
has a corresponding SHLIBOBJS-$(CONFIG_MXF_MUXER) += golomb_tab.o.
E.g. in case the MXF muxer is the only component needing these tables
only libavformat.so will contain them for shared builds; currently
libavcodec.so does so, too.
(There is currently a CONFIG_EXTRA group for golomb. But actually
one would need two groups (golomb_avcodec and golomb_avformat) in
order to know when and where to include these tables. Therefore
this commit uses a Makefile-based approach for this and stops
using these groups for the users in libavformat.)
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
They test libavfilter internal API, so they should be libavfilter
test programs (which implies: linked statically to libavfilter
to access internal APIs and linked normally (statically or dynamically
depending upon the build configuration) against all the other libs).
Right now, they are always linked statically against all libs,
which is a significant size waste compared to shared libs as all
of libavcodec has been pulled in despite not being really used.
This also leads to linking failures on systems for which av_export_avutil
is intended: libavcodec does not expect to be linked statically
against the library providing avpriv_(cga|vga16)_font in this case.
This is fixed by this commit.
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>
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>
The issue is that libavfilter depends on libavcodec, and when doing a
static build, if libavcodec also includes "libavfilter/vulkan.c", then
during link-time, compiling programs will fail as there would be multiple
definitions of the same symbols in both libavfilter and libavcodec's
object files.
Linkers are, however, more permitting if both files that include
a common file that's used as a template are one-to-one identical.
Hence, to make both files the same in the future, export all avfilter
specific functions to a separate file.
There is some work in progress to make templated files like this be
compiled only once, so this is not a long-term solution.
This also removes a macro that could be used to toggle SPIRV compilation
capability on #include-time, as this could cause the files to be different.
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>
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>
Paul B Mahol
Boris Baracaldo
Martin Storsjö
Xinpeng Sun
Wu Jianhua
Andreas Rheinhardt
Aman Karmani
Lynne
Niklas Haas
Roman Arzumanyan
Paul Buxton
Timo Rothenpieler
Xuewei Meng
Guo, Yejun
Andreas Rheinhardt
Ashish Singh
