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Autres articles (73)

  • MediaSPIP v0.2

    21 juin 2013, par

    MediaSPIP 0.2 est la première version de MediaSPIP stable.
    Sa date de sortie officielle est le 21 juin 2013 et est annoncée ici.
    Le fichier zip ici présent contient uniquement les sources de MediaSPIP en version standalone.
    Comme pour la version précédente, il est nécessaire d’installer manuellement l’ensemble des dépendances logicielles sur le serveur.
    Si vous souhaitez utiliser cette archive pour une installation en mode ferme, il vous faudra également procéder à d’autres modifications (...)

  • Creating farms of unique websites

    13 avril 2011, par

    MediaSPIP platforms can be installed as a farm, with a single "core" hosted on a dedicated server and used by multiple websites.
    This allows (among other things) : implementation costs to be shared between several different projects / individuals rapid deployment of multiple unique sites creation of groups of like-minded sites, making it possible to browse media in a more controlled and selective environment than the major "open" (...)

  • Mise à disposition des fichiers

    14 avril 2011, par

    Par défaut, lors de son initialisation, MediaSPIP ne permet pas aux visiteurs de télécharger les fichiers qu’ils soient originaux ou le résultat de leur transformation ou encodage. Il permet uniquement de les visualiser.
    Cependant, il est possible et facile d’autoriser les visiteurs à avoir accès à ces documents et ce sous différentes formes.
    Tout cela se passe dans la page de configuration du squelette. Il vous faut aller dans l’espace d’administration du canal, et choisir dans la navigation (...)

Sur d’autres sites (6450)

  • avformat/matroskaenc : Check for reformatting errors

    23 janvier 2020, par Andreas Rheinhardt
    avformat/matroskaenc : Check for reformatting errors

    This is needed especially for AV1 : If a reformatting error happens (e.g.
    
if the length field of an OBU contained in the current packet indicates
    
that said OBU extends beyond the current packet), the data pointer is
    
still NULL, yet the size is unchanged, so that writing the data leads
    
to a segmentation fault.
    

    Signed-off-by : Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
    
Signed-off-by : James Almer <jamrial@gmail.com>
    • [DH] libavformat/matroskaenc.c

  • video processing on android using ndk with ffmpeg and opencv is very slow

    22 septembre 2017, par Zhiqiang Li

    I’m doing some video processing on android using ndk.

    What I’m doing now is decode the video using ffmpeg, grab the frame, convert it to opencv mat, then overlay some images on it, then convert it back to avframe, encode it back to a video.

    But it’s very slow ! It takes about 50 seconds to process a 480*480 video with 15 fps.

    I tried to do this using opengl es, reading the result image using glReadPixels, but from what I understand, glReadPixels doesn’t really work on some devices. So I had to give it up.

    As I understand, I have some very expensive operations in my current workflow,

    1. covert a AVFrame from yuv to bgr color space, then convert to opencv mat
    2. overlay a mat on another mat
    3. covert a opencv mat to AVFrame, then convert the frame from bgr to yuv space, then encode it into a video.

    So, are there ways to improve my current workflow ?

    I’m adding multithread feature, but only devices with multicore cup can benefit from that.

  • video processing on android using ndk with ffmpeg and opencv is very slow

    26 novembre 2014, par Zhiqiang Li

    I’m doing some video processing on android using ndk.

    What I’m doing now is decode the video using ffmpeg, grab the frame, convert it to opencv mat, then overlay some images on it, then convert it back to avframe, encode it back to a video.

    But it’s very slow ! It takes about 50 seconds to process a 480*480 video with 15 fps.

    I tried to do this using opengl es, reading the result image using glReadPixels, but from what I understand, glReadPixels doesn’t really work on some devices. So I had to give it up.

    As I understand, I have some very expensive operations in my current workflow,

    1. covert a AVFrame from yuv to bgr color space, then convert to opencv mat
    2. overlay a mat on another mat
    3. covert a opencv mat to AVFrame, then convert the frame from bgr to yuv space, then encode it into a video.

    So, are there ways to improve my current workflow ?

    I’m adding multithread feature, but only devices with multicore cup can benefit from that.

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