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Médias (1)
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Collections - Formulaire de création rapide
19 février 2013, par
Mis à jour : Février 2013
Langue : français
Type : Image
Autres articles (31)
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Support de tous types de médias
10 avril 2011Contrairement à beaucoup de logiciels et autres plate-formes modernes de partage de documents, MediaSPIP a l’ambition de gérer un maximum de formats de documents différents qu’ils soient de type : images (png, gif, jpg, bmp et autres...) ; audio (MP3, Ogg, Wav et autres...) ; vidéo (Avi, MP4, Ogv, mpg, mov, wmv et autres...) ; contenu textuel, code ou autres (open office, microsoft office (tableur, présentation), web (html, css), LaTeX, Google Earth) (...)
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D’autres logiciels intéressants
12 avril 2011, parOn ne revendique pas d’être les seuls à faire ce que l’on fait ... et on ne revendique surtout pas d’être les meilleurs non plus ... Ce que l’on fait, on essaie juste de le faire bien, et de mieux en mieux...
La liste suivante correspond à des logiciels qui tendent peu ou prou à faire comme MediaSPIP ou que MediaSPIP tente peu ou prou à faire pareil, peu importe ...
On ne les connais pas, on ne les a pas essayé, mais vous pouvez peut être y jeter un coup d’oeil.
Videopress
Site Internet : (...) -
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 (...)
Sur d’autres sites (4954)
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AWS Rekognition error : Chunk video failed
14 juin 2022, par Stefano LeoneI'm using and launching Amazon Rekognition on my videos uploaded into my S3 with python. Every video is converted with FFMPEG with
AACAudio Codec andH264Video Codec and then given to Rekognition. Videos are always fine, the problem is that only 60-70% of videos are processed successfully, while the rest of videos goes into error. In particular, inside the JSON returned from Rekognition :

{'JobId': '<id of="of" my="my" job="job">', 'Status': 'FAILED', 'API': 'StartFaceDetection', 'Message': 'Chunk video failed: Only 1 I-frames found in video', 'Timestamp': 1655118632996, 'Video': {'S3ObjectName': '<my video="video" inside="inside" s3="s3">', 'S3Bucket': '<my s3="s3">'}, 'ErrorCode': 'VideoNotDecodable'}
</my></my></id>

As you can see, I get an error
"Chunk video failed: only 1 I-Frames found in video". Honestly I don't know what is that, but the fact that I convert every video in the same way, but Rekognition fails only with some, makes me crazy. Googling was not helpful, hope you can tell me what's wrong.

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Thumbnails from S3 Videos using FFMPEG - "No such file or directory : '/bin/ffmpeg'"
28 juin 2022, par NicoI am trying to generate thumbnails from videos in an S3 bucket every x frames by following this documentation : https://aws.amazon.com/blogs/media/processing-user-generated-content-using-aws-lambda-and-ffmpeg/


I am at the point where I'm testing the Lambda code provided in the documentation, but receive this error in CloudWatch Logs :




Here is the portion of the Lambda code associated with this error :




Any help is appreciated. Thanks !


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FFMPEG on Heroku exceeds memory quota in testing
5 juillet 2022, par Patrick VelliaAfter following this tutorial, and getting it to work locally on my own development environment, before really getting my hands dirty and working deeper on my own project implementation, I decided to push it up to Heroku to test in a staging environment.


I had to have Heroku add the FFMPEG build-pack and turn on the Redis Server for ActionCable to work.


I didn't link the staging to a cloud storage bucket on Google or Amazon yet, just allowed it to upload directly to the dymo disk for testing. So it would go into the storage directory as it would in development for now.


the test MOV file is 186 MB in size.


The system uploaded the file fine.


According to the logs, it then copied the file from storage to tmp as the tutorial has us do.


Then it called streamio-ffmpeg's transcode method.


At this point, Heroku forcibly kills the dymo because it far exceeds the memory quota.


As this is a test environment, it's only on the free tier of Heroku.


I'm thinking I won't be able to directly process video projects on Heroku itself, unless I'm wrong ? Would it be better to call an API like Cloud Functions or Amazon Lambda, or spin up a Compute Engine long enough to process the FFMPEG command ?


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