Two modes are supported in guided filter, basic mode and fast mode.
Basic mode is the initial pushed guided filter without optimization.
Fast mode is implemented based on the basic one by sub-sampling method.
The sub-sampling ratio which can be defined by users controls the
algorithm complexity. The larger the sub-sampling ratio, the lower
the algorithm complexity.
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
Reviewed-by: Steven Liu <liuqi05@kuaishou.com>
commit 95b854dd06 "rename sum option to normalize" missed command
part docs
Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
Reviewed-by: Gyan Doshi <ffmpeg@gyani.pro>
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 also allows to exclusively use pointers to const AVCodec in
fftools/ffmpeg_opt.c.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.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>
Deprecated in d682ae70b4,
ineffective since ca4df37f06.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
Signed-off-by: James Almer <jamrial@gmail.com>
Several options that were too codec-specific were deprecated between
0e6c853221 and
0e9c4fe254.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
Signed-off-by: James Almer <jamrial@gmail.com>
The build log:
** Unknown command `@code' (left as is) (in src/doc/muxers.texi l. 2020)
*** '{' without macro. Before: -map} option with the ffmpeg CLI tool. (in src/doc/muxers.texi l. 2020)
*** '}' without opening '{' before: option with the ffmpeg CLI tool. (in src/doc/muxers.texi l. 2020)
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>
Remove the unneeded wrapping sequence element. Also the
minOccurs/maxOccurs occurrence indicators on the inner element
definitions can be removed.
Signed-off-by: Tobias Rapp <t.rapp@noa-archive.com>
This avoids crafted files from consuming excessive resources recomputing the clut after each pixel change
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Xuewei Meng
Jun Zhao
Xuewei Meng
Gyan Doshi
Cameron Cawley
Guo, Yejun
Limin Wang
James Almer
Anton Khirnov
Andreas Rheinhardt
Andreas Rheinhardt
Zane van Iperen
Lynne
Tobias Rapp
Andriy Gelman
Michael Niedermayer
