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Médias (1)
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DJ Dolores - Oslodum 2004 (includes (cc) sample of “Oslodum” by Gilberto Gil)
15 septembre 2011, par
Mis à jour : Septembre 2011
Langue : English
Type : Audio
Autres articles (104)
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Gestion des droits de création et d’édition des objets
8 février 2011, parPar défaut, beaucoup de fonctionnalités sont limitées aux administrateurs mais restent configurables indépendamment pour modifier leur statut minimal d’utilisation notamment : la rédaction de contenus sur le site modifiables dans la gestion des templates de formulaires ; l’ajout de notes aux articles ; l’ajout de légendes et d’annotations sur les images ;
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Supporting all media types
13 avril 2011, parUnlike most software and media-sharing platforms, MediaSPIP aims to manage as many different media types as possible. The following are just a few examples from an ever-expanding list of supported formats : images : png, gif, jpg, bmp and more audio : MP3, Ogg, Wav and more video : AVI, MP4, OGV, mpg, mov, wmv and more text, code and other data : OpenOffice, Microsoft Office (Word, PowerPoint, Excel), web (html, CSS), LaTeX, Google Earth and (...)
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Keeping control of your media in your hands
13 avril 2011, parThe vocabulary used on this site and around MediaSPIP in general, aims to avoid reference to Web 2.0 and the companies that profit from media-sharing.
While using MediaSPIP, you are invited to avoid using words like "Brand", "Cloud" and "Market".
MediaSPIP is designed to facilitate the sharing of creative media online, while allowing authors to retain complete control of their work.
MediaSPIP aims to be accessible to as many people as possible and development is based on expanding the (...)
Sur d’autres sites (11162)
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What's the best FFMPEG method for frequent, automated compilation of timelapse videos ?
5 août 2020, par GoOutsideI have a web application running on a not-particularly beefy Ubuntu Amazon Lightsail instance that uses FFMPEG to build a timelapse video generated from downloaded .jpg webcam photos taken every 2 minutes throughout the day (720 total images each day, which grows throughout the day as new images are downloaded).


The code I'm running every 20 minutes is this :


ffmpeg -y -r 24 -pattern_type glob -I 'picturefolder/*.jpg' -s 1024x576 -vcodec libx264 picturefolder/timelapse.mp4


This mostly works, but it is often quite slow, taking 30-60 seconds to run and getting slower as the day goes on, of course.


Recently, I tried to use
concat
instead ofglob
bing the entire folder over and over. I did not see a noticeable performance improvement, ass it appears theconcat
processes the entire video in order to add even just a few frames to the end of it.

My question for any FFMPEG experts out there : what is the most efficient way to handle this kind of automated timelapse creation, given my setup ? Is there a flag I'm missing ? Perhaps a different, more efficient method ? Or maybe a way to have the FFMPEG process just crawl through this at a more 'slow and steady' pace instead of big bursts of CPU usage.


Or am I stuck with this and should just deal with it ? My ultimate goal would be to continue using my current tier (2 GB RAM, 1 vCPU) without the expense of upgrading. Thank you very kindly for your help !


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Error : ffmpeg exited with code 1 on AWS Lambda
16 juin 2022, par Hassnain AlviI am using fluent-ffmpeg nodejs package to run ffmpeg for audio conversion on AWS Lambda. I am using this FFmpeg layer for lambda.
Here is my code


const bitrate64 = ffmpeg("file.mp3").audioBitrate('64k');
 bitrate64.outputOptions([
 '-preset slow',
 '-g 48',
 "-map", "0:0",
 '-hls_time 6',
 '-master_pl_name master.m3u8',
 '-hls_segment_filename 64k/fileSequence%d.ts'
 ])
 .output('./64k/prog_index.m3u8')
 .on('progress', function(progress) {
 console.log('Processing 64k bitrate: ' + progress.percent + '% done')
 }) 
 .on('end', function(err, stdout, stderr) {
 console.log('Finished processing 64k bitrate!')
 })
 .run() 



after running it via AWS lambda I get following error message


ERROR Uncaught Exception 
{
 "errorType": "Error",
 "errorMessage": "ffmpeg exited with code 1: Conversion failed!\n",
 "stack": [
 "Error: ffmpeg exited with code 1: Conversion failed!",
 "",
 " at ChildProcess.<anonymous> (/var/task/node_modules/fluent-ffmpeg/lib/processor.js:182:22)",
 " at ChildProcess.emit (events.js:198:13)",
 " at ChildProcess.EventEmitter.emit (domain.js:448:20)",
 " at Process.ChildProcess._handle.onexit (internal/child_process.js:248:12)"
 ]
}
</anonymous>


I don't get any more info so I am not sure what's going on. Can anyone tell me what's wrong here and how can I enable more detailed logs ?


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Using ffmpeg to assemble images from S3 into a video
10 juillet 2020, par Mass Dot NetI can easily assemble images from local disk into a video using ffmpeg and passing a
%06d
filespec. Here's what a typical (pseudocode) command would look like :

ffmpeg.exe -hide_banner -y -r 60 -t 12 -i /JpgsToCombine/%06d.JPG <..etc..>



However, I'm struggling to do the same with images stored in AWS S3, without using some third party software to mount a virtual drive (e.g. TNTDrive). The S3 folder containing our images is too large to download to the 20GB ephemeral storage provided for AWS containers, and we're trying to avoid EFS because we'd have to provision expensive bandwidth.


Here's what the HTTP and S3 URLs to each of our JPGs looks like :


# HTTP URL
https://massdotnet.s3.amazonaws.com/jpgs-to-combine/000000.JPG # frame 0
https://massdotnet.s3.amazonaws.com/jpgs-to-combine/000012.JPG # frame 12
https://massdotnet.s3.amazonaws.com/jpgs-to-combine/000123.JPG # frame 123
https://massdotnet.s3.amazonaws.com/jpgs-to-combine/456789.JPG # frame 456789

# S3 URL
s3://massdotnet/jpgs-to-combine/000000.JPG # frame 0
s3://massdotnet/jpgs-to-combine/000012.JPG # frame 12
s3://massdotnet/jpgs-to-combine/000123.JPG # frame 123
s3://massdotnet/jpgs-to-combine/456789.JPG # frame 456789



Is there any way to get ffmpeg to assemble these ? We could generate a signed URL for each S3 file, and put several thousand of those URLs onto a command line with an FFMPEG concat filter. However, we'd run up into the command line input limit in Linux at some point using this approach. I'm hoping there's a better way...