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author | Guo, Yejun <yejun.guo@intel.com> | 2021-02-07 14:36:13 +0800 |
---|---|---|
committer | Guo, Yejun <yejun.guo@intel.com> | 2021-04-17 17:27:02 +0800 |
commit | aa9ffdaa1eaeb5e16fb6b89852f38ff488d81173 (patch) | |
tree | 85afb97148ad11be2cf30d346fe91db448dd0faa /doc | |
parent | e942b4bbaaddad451752254cbb60a3ea383294d6 (diff) |
lavfi: add filter dnn_detect for object detection
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>
Diffstat (limited to 'doc')
-rw-r--r-- | doc/filters.texi | 40 |
1 files changed, 40 insertions, 0 deletions
diff --git a/doc/filters.texi b/doc/filters.texi index 5e35fa6467..68f17dd563 100644 --- a/doc/filters.texi +++ b/doc/filters.texi @@ -10127,6 +10127,46 @@ ffmpeg -i INPUT -f lavfi -i nullsrc=hd720,geq='r=128+80*(sin(sqrt((X-W/2)*(X-W/2 @end example @end itemize +@section dnn_detect + +Do object detection with deep neural networks. + +The filter accepts the following options: + +@table @option +@item dnn_backend +Specify which DNN backend to use for model loading and execution. This option accepts +only openvino now, tensorflow backends will be added. + +@item model +Set path to model file specifying network architecture and its parameters. +Note that different backends use different file formats. + +@item input +Set the input name of the dnn network. + +@item output +Set the output name of the dnn network. + +@item confidence +Set the confidence threshold (default: 0.5). + +@item labels +Set path to label file specifying the mapping between label id and name. +Each label name is written in one line, tailing spaces and empty lines are skipped. +The first line is the name of label id 0 (usually it is 'background'), +and the second line is the name of label id 1, etc. +The label id is considered as name if the label file is not provided. + +@item backend_configs +Set the configs to be passed into backend + +@item async +use DNN async execution if set (default: set), +roll back to sync execution if the backend does not support async. + +@end table + @anchor{dnn_processing} @section dnn_processing |