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SWFUpload Process
6 septembre 2011, par
Mis à jour : Septembre 2011
Langue : français
Type : Texte
Autres articles (73)
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Submit bugs and patches
13 avril 2011Unfortunately a software is never perfect.
If you think you have found a bug, report it using our ticket system. Please to help us to fix it by providing the following information : the browser you are using, including the exact version as precise an explanation as possible of the problem if possible, the steps taken resulting in the problem a link to the site / page in question
If you think you have solved the bug, fill in a ticket and attach to it a corrective patch.
You may also (...) -
MediaSPIP 0.1 Beta version
25 avril 2011, parMediaSPIP 0.1 beta is the first version of MediaSPIP proclaimed as "usable".
The zip file provided here only contains the sources of MediaSPIP in its standalone version.
To get a working installation, you must manually install all-software dependencies on the server.
If you want to use this archive for an installation in "farm mode", you will also need to proceed to other manual (...) -
MediaSPIP v0.2
21 juin 2013, parMediaSPIP 0.2 is the first MediaSPIP stable release.
Its official release date is June 21, 2013 and is announced here.
The zip file provided here only contains the sources of MediaSPIP in its standalone version.
To get a working installation, you must manually install all-software dependencies on the server.
If you want to use this archive for an installation in "farm mode", you will also need to proceed to other manual (...)
Sur d’autres sites (9074)
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lavfi : add filter dnn_detect for object detection
7 février 2021, par Guo, Yejunlavfi : 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>
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libavcodec/jpeg2000dec.c : ROI marker support
23 avril 2020, par Gautam Ramakrishnanlibavcodec/jpeg2000dec.c : ROI marker support
This patch adds support for decoding images
with a Region of Interest. Allows decoding
samples such as p0_03.j2k. This patch should
fix ticket #4681.Signed-off-by : Michael Niedermayer <michael@niedermayer.cc>
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lavfi/dnn_classify : add filter dnn_classify for classification based on detection...
17 mars 2021, par Guo, Yejunlavfi/dnn_classify : add filter dnn_classify for classification based on detection bounding boxes
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.labelrun 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>