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Exemple de boutons d’action pour une collection collaborative
27 février 2013, par
Mis à jour : Mars 2013
Langue : français
Type : Image
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Exemple de boutons d’action pour une collection personnelle
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Mis à jour : Février 2013
Langue : English
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Collections - Formulaire de création rapide
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Mis à jour : Février 2013
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Autres articles (28)
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La file d’attente de SPIPmotion
28 novembre 2010, parUne file d’attente stockée dans la base de donnée
Lors de son installation, SPIPmotion crée une nouvelle table dans la base de donnée intitulée spip_spipmotion_attentes.
Cette nouvelle table est constituée des champs suivants : id_spipmotion_attente, l’identifiant numérique unique de la tâche à traiter ; id_document, l’identifiant numérique du document original à encoder ; id_objet l’identifiant unique de l’objet auquel le document encodé devra être attaché automatiquement ; objet, le type d’objet auquel (...) -
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 -
Contribute to documentation
13 avril 2011Documentation is vital to the development of improved technical capabilities.
MediaSPIP welcomes documentation by users as well as developers - including : critique of existing features and functions articles contributed by developers, administrators, content producers and editors screenshots to illustrate the above translations of existing documentation into other languages
To contribute, register to the project users’ mailing (...)
Sur d’autres sites (5251)
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extract audio with ffmpeg
20 décembre 2014, par marcobonny88I need to extract audio from many mov file. I have to utilize the audios inside an iOS app, the goal is to have outputs file with low file size weight and perfectly equal sound to the input file.
So i proceeded extrating audio in this way :ffmpeg -i 6.2.mov -vn -acodec aac -strict -2 try.m4a
The input information :
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '6.2.mov':
Metadata:
major_brand : qt
minor_version : 0
compatible_brands: qt
creation_time : 2014-12-03 10:12:08
Duration: 00:00:01.58, start: 0.000000, bitrate: 42754 kb/s
Stream #0:0(und): Video: prores (apcn / 0x6E637061), yuv422p10le(bt709), 960x540, 40383 kb/s, SAR 1:1 DAR 16:9, 23.98 fps, 23.98 tbr, 24k tbn, 24k tbc (default)
Metadata:
creation_time : 2014-12-03 10:12:08
handler_name : Core Media Data Handler
encoder : Apple ProRes 422
timecode : 00:00:00:00
Stream #0:1(und): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, stereo, s32 (24 bit), 2304 kb/s (default)
Metadata:
creation_time : 2014-12-03 10:12:08
handler_name : Core Media Data Handler
Stream #0:2(und): Data: none (tmcd / 0x64636D74), 0 kb/s (default)
Metadata:
creation_time : 2014-12-03 10:12:08
handler_name : Core Media Data Handler
timecode : 00:00:00:00The output information :
Output #0, ipod, to 'try.m4a':
Metadata:
major_brand : qt
minor_version : 0
compatible_brands: qt
encoder : Lavf56.15.102
Stream #0:0(und): Audio: aac (mp4a / 0x6134706D), 48000 Hz, stereo, fltp (24 bit), 92 kb/s (default)
Metadata:
creation_time : 2014-12-03 10:12:08
handler_name : Core Media Data Handler
encoder : Lavc56.13.100 aac
Stream mapping:
Stream #0:1 -> #0:0 (pcm_s24le (native) -> aac (native))The problem is that re encoding pcm_s24le to AAC create an output that cut the some ms at the beginning.
There is a way resolve or go around the problem ? Like padding the output 50ms...
Helps ? -
Fastest way to extract moving dynamic crop from video using ffmpeg
5 avril 2024, par NateWI'm working on an AI project that involves object detection and action recognition on roughly 30 minute videos.


My pipeline is the following :


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- determine crops using object detection model
- extract crops using Python and write them to disk as individual images for each frame.
- use action recognition model by inputting a series of the crops.








The models are fast but actual writing of the crops to disk is slow. Sure, using an SSD would speed it up but I'm sure ffmpeg would greatly speed it up.


Some of the challenges with the crops :


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- the output size is always 128x128
- the input size is variable
- the crop moves on every frame








My process for extracting crops is simple using
cv2.imwrite(output_crop_path, crop)
in a for loop.

I've done experiments trying to use
sndcommand
andfilter_complex
. I tried this https://stackoverflow.com/a/67508233/4447761 but it outputs an image with black below the crop and the image gets wrapped around on the x axis.



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How to extract video clip from larger video based on specific time and duration
26 juillet 2022, par user19019404I have video clips that get created. Each video is recorded for 5 minutes and starts at a time e.g. 10:01:20 to 10:06:19 then the next video from 10:06:20 etc. These videos are recorded at a specific frame rate, be it 5 frames or 30 frames (its dependent on the platform making the recording). The net result is NOT a 5 minute video clip but might be a 2 minute video clip (where everyone moves very quickly in the view as a result of the frame rates). I cannot restrict a 1 to 1 recording as these are generated by external systems.


I need to extract specific portions out of the video. For example I need to extract from 10:03:10 to 10:03:35 (25 seconds). This would equate to 10 seconds into the video up to 14 seconds into the video, in those 4 seconds of video, 25 real world seconds are displayed.


My question is do you have any guidance as to how I can calculate that each second of recording actually means 10 or 12 seconds in real life, therefore go to this frame and record to this frame for example.


I have been looking at cv2.CAP_PROP_FPS to get the video frame rates, CAP_PROP_POS_MSEC and CAP_PROP_FRAME_COUNT and believe the answer might lie there, but not sure.


The thinking being if I work out the frame rate of the video, the total frames then I can divide the one by the other to get to how many frames make up real world seconds (this is where I fail). This way I can run from frame to frame as a result.


Thank you