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Carte de Schillerkiez
13 mai 2011, par
Mis à jour : Septembre 2011
Langue : English
Type : Texte
Autres articles (67)
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Publier sur MédiaSpip
13 juin 2013Puis-je poster des contenus à partir d’une tablette Ipad ?
Oui, si votre Médiaspip installé est à la version 0.2 ou supérieure. Contacter au besoin l’administrateur de votre MédiaSpip pour le savoir -
HTML5 audio and video support
13 avril 2011, parMediaSPIP uses HTML5 video and audio tags to play multimedia files, taking advantage of the latest W3C innovations supported by modern browsers.
The MediaSPIP player used has been created specifically for MediaSPIP and can be easily adapted to fit in with a specific theme.
For older browsers the Flowplayer flash fallback is used.
MediaSPIP allows for media playback on major mobile platforms with the above (...) -
Support audio et vidéo HTML5
10 avril 2011MediaSPIP utilise les balises HTML5 video et audio pour la lecture de documents multimedia en profitant des dernières innovations du W3C supportées par les navigateurs modernes.
Pour les navigateurs plus anciens, le lecteur flash Flowplayer est utilisé.
Le lecteur HTML5 utilisé a été spécifiquement créé pour MediaSPIP : il est complètement modifiable graphiquement pour correspondre à un thème choisi.
Ces technologies permettent de distribuer vidéo et son à la fois sur des ordinateurs conventionnels (...)
Sur d’autres sites (8273)
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ffmpeg segment naming - Moving to superuser (with apologies)
21 juillet 2022, par kenneth558Maybe I am misinterpreting the downvote, but I conclude this question should go on superuser instead. Moving now and will delete this copy soon....


I need to ensure unique segment names : Apparent POE cable defects, etc. around campus cause HikVision camera streams to require their ffmpeg daemons re-started once or twice or more times/day. (I am miles away from this campus for the most part, so I prefer a command line fix until the hardware fixes get applied.) When ffmpeg has to be restarted for a camera (by background bash script), I need the names of the new
.mp4
segments positively not to be the same as any previous names.

Background bash process currently does fine to specify an acceptable ddHHMM style new starting name for the first segment after ffmpeg restart BUT after the first or sometime second or third segment is made, ffmpeg insists on future naming to default to an unacceptable YYYmmdd style and thus start to overwrite previous segments. I use
"$(date +%d%H%M)"
to obtain my acceptable date style.

I've tried a lot of different combinations of date codes and date embedding and both
ssegment
andsegment
muxers ; also I know very little of the very complex realm that ffmpeg is normally used in outside of simple rtsp stream copy to.mp4
files.

ffmpeg command that is launched from inside bash script :

bash -c 'nohup ffmpeg -nostdin -stimeout 10000000 -rtsp_transport udp -i "rtsp://192.168.0.11:6554/Streaming/channels/101" -reconnect 1 -reconnect_at_eof 1 -reconnect_streamed 1 -c:v libx264 -f ssegment -strftime 0 -segment_time 180 -segment_format_options movflags=+faststart -reset_timestamps 0 -increment_tc 1 -avoid_negative_ts 1 -c copy -flags +global_header /var/www/camera_streams/camera_east_driveway/"$(date +%d%H%M)"_%3d.mp4 > /dev/null 2>/dev/null & '


Can the segment naming pattern be carried forward indefinitely like I want ? Honestly, I wonder if ffmpeg does not allow for my specific use case naming need ?


(Yes, I know changing from
udp
totcp
can help, but I don't consider it to be the specific solid naming fix I'm hoping for right now. And I mention HikVision in case there is known frame encoding differences for them than other cameras)

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qsv : Move down the implementation query
24 février 2016, par Luca Barbato -
dnn/vf_dnn_detect.c : add tensorflow output parse support
6 mai 2021, par Ting Fudnn/vf_dnn_detect.c : add tensorflow output parse support
Testing model is tensorflow offical model in github repo, please refer
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
to download the detect model as you need.
For example, local testing was carried on with 'ssd_mobilenet_v2_coco_2018_03_29.tar.gz', and
used one image of dog in
https://github.com/tensorflow/models/blob/master/research/object_detection/test_images/image1.jpgTesting command is :
./ffmpeg -i image1.jpg -vf dnn_detect=dnn_backend=tensorflow:input=image_tensor:output=\
"num_detections&detection_scores&detection_classes&detection_boxes":model=ssd_mobilenet_v2_coco.pb,\
showinfo -f null -We will see the result similar as below :
[Parsed_showinfo_1 @ 0x33e65f0] side data - detection bounding boxes :
[Parsed_showinfo_1 @ 0x33e65f0] source : ssd_mobilenet_v2_coco.pb
[Parsed_showinfo_1 @ 0x33e65f0] index : 0, region : (382, 60) -> (1005, 593), label : 18, confidence : 9834/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index : 1, region : (12, 8) -> (328, 549), label : 18, confidence : 8555/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index : 2, region : (293, 7) -> (682, 458), label : 1, confidence : 8033/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index : 3, region : (342, 0) -> (690, 325), label : 1, confidence : 5878/10000.There are two boxes of dog with cores 94.05% & 93.45% and two boxes of person with scores 80.33% & 58.78%.
Signed-off-by : Ting Fu <ting.fu@intel.com>
Signed-off-by : Guo, Yejun <yejun.guo@intel.com>