In dd770883e9, support for expressions was added. Among the constants
added were labels of qnstc, qpal, sntsc & spal.
These were added in ba2a8cb40b to represent parameter permutations where
only the resolution is different. They don't have any usage currency and
don't represent any industry standards or convention in terms of framerate.
Support single input for guided filter by adding guidance mode.
If the guidance mode is off, single input is required. And
edge-preserving smoothing is conducted. If the mode is on, two
inputs are needed. The second input serves as the guidance. For
this mode, more tasks are supported, such as detail enhancement,
dehazing and so on.
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
Reviewed-by: Steven Liu <lq@chinaffmpeg.org>
This feature can be used with dnn detection by setting vf_drawtext's option
text_source=side_data_detection_bboxes, for example:
./ffmpeg -i face.jpeg -vf dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:\
input=data:output=detection_out:labels=face-detection-adas-0001.label,drawbox=box_source=
side_data_detection_bboxes,drawtext=text_source=side_data_detection_bboxes:fontcolor=green:\
fontsize=40, -y face_detect.jpeg
Please note, the default fontsize of vf_drawtext is 12, which may be too
small to be seen clearly.
Signed-off-by: Ting Fu <ting.fu@intel.com>
This feature can be used with dnn detection by setting vf_drawbox's
option box_source=side_data_detection_bboxes, for example:
./ffmpeg -i face.jpeg -vf dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:\
input=data:output=detection_out:labels=face-detection-adas-0001.label,\
drawbox=box_source=side_data_detection_bboxes -y face_detect.jpeg
Signed-off-by: Ting Fu <ting.fu@intel.com>
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>
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>
Gyan Doshi
James Almer
Paul B Mahol
Xuewei Meng
Timo Rothenpieler
Ting Fu
Jun Zhao
Xuewei Meng
Guo, Yejun
Limin Wang
Ashish Singh
Jan Ekström
