Recherche avancée

Médias (0)

Mot : - Tags -/alertes

Aucun média correspondant à vos critères n’est disponible sur le site.

Autres articles (77)

  • 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 (...)

  • 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 (...)

  • Le profil des utilisateurs

    12 avril 2011, par

    Chaque utilisateur dispose d’une page de profil lui permettant de modifier ses informations personnelle. Dans le menu de haut de page par défaut, un élément de menu est automatiquement créé à l’initialisation de MediaSPIP, visible uniquement si le visiteur est identifié sur le site.
    L’utilisateur a accès à la modification de profil depuis sa page auteur, un lien dans la navigation "Modifier votre profil" est (...)

Sur d’autres sites (6818)

  • FFmpeg matlab error : At least one output file must be specified ? [closed]

    3 mars, par as moh

    I'm trying to get I frames from a video using Matlab using this command system(sprintf('ffmpeg -i testVid.mp4 -vf "select=eq(pict_type\,I)" -vsync vfr output_%03d.png')); ,but i get this message

    


     ffmpeg version 7.1-full_build-www.gyan.dev Copyright (c) 2000-2024 the FFmpeg developers 
  built with gcc 14.2.0 (Rev1, Built by MSYS2 project) 
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libopenjpeg --enable-libquirc --enable-libuavs3d --enable-libxevd --enable-libzvbi --enable-libqrencode --enable-librav1e --enable-libsvtav1 --enable-libvvenc --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxeve --enable-libxvid --enable-libaom --enable-libjxl --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-dxva2 --enable-d3d11va --enable-d3d12va --enable-ffnvcodec --enable-libvpl --enable-nvdec --enable-nvenc --enable-vaapi --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-liblc3 --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint 
  libavutil      59. 39.100 / 59. 39.100 
  libavcodec     61. 19.100 / 61. 19.100 
  libavformat    61.  7.100 / 61.  7.100 
  libavdevice    61.  3.100 / 61.  3.100 
  libavfilter    10.  4.100 / 10.  4.100 
  libswscale      8.  3.100 /  8.  3.100 
  libswresample   5.  3.100 /  5.  3.100 
  libpostproc    58.  3.100 / 58.  3.100 
Trailing option(s) found in the command: may be ignored. 
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'testVid.mp4': 
  Metadata: 
    major_brand     : isom 
    minor_version   : 512 
    compatible_brands: isomiso2avc1mp41 
    encoder         : Lavf57.83.100 
  Duration: 00:00:02.02, start: 0.000000, bitrate: 12798 kb/s 
  Stream #0:0[0x1](eng): Video: h264 (Baseline) (avc1 / 0x31637661), yuvj420p(pc, progressive), 1280x720 [SAR 1:1 DAR 16:9], 12662 kb/s, 29.74 fps, 30 tbr, 90k tbn (default) 
      Metadata: 
        handler_name    : VideoHandler 
        vendor_id       : [0][0][0][0] 
  Stream #0:1[0x2](eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, mono, fltp, 121 kb/s (default) 
      Metadata: 
        handler_name    : SoundHandler 
        vendor_id       : [0][0][0][0] 
At least one output file must be specified 


    


    i searched and tried many cases but i don't know where is the problem, any help please ?

    


  • Revision 34808 : class url pour le lien et non org (site VS société, merci tetue)

    31 janvier 2010, par brunobergot@… — Log

    class url pour le lien et non org (site VS société, merci tetue)

  • Introducing the BigQuery & Data Warehouse Export feature

    30 janvier, par Matomo Core Team

    Matomo is built on a simple truth : your data belongs to you, and you should have complete control over it. That’s why we’re excited to launch our new BigQuery & Data Warehouse Export feature for Matomo Cloud, giving you even more ways to work with your analytics data. 

    Until now, getting raw data from Matomo Cloud required APIs and custom scripts, or waiting for engineering help.  

    Our new BigQuery & Data Warehouse Export feature removes those barriers. You can now access your raw, unaggregated data and schedule regular exports straight to your data warehouse. 

    The feature works with all major data warehouses including (but not limited to) : 

    • Google BigQuery 
    • Amazon Redshift 
    • Snowflake 
    • Azure Synapse Analytics 
    • Apache Hive 
    • Teradata 

    You can schedule exports, combine your Matomo data with other data sources in your data warehouse, and easily query data with SQL-like queries. 

    Direct raw data access for greater data portability 

    Waiting for engineering support can delay your work. Managing API connections and writing scripts can be time-consuming. This keeps you from focusing on what you do best—analysing data. 

    BigQuery create-table-menu

    With the BigQuery & Data Warehouse Export feature, you get direct access to your raw Matomo data without the technical setup. So, you can spend more time analysing data and finding insights that matter. 

    Bringing your data together 

    Answering business questions often requires data from multiple sources. A single customer interaction might span your CRM, web analytics, sales systems, and more. Piecing this data together manually is time-consuming—what starts as a seemingly simple question from stakeholders can turn into hours of work collecting and comparing data across different tools. 

    This feature lets you combine your Matomo data with data from other business systems in your data warehouse. Instead of switching between tools or manually comparing spreadsheets, you can analyse all your data in one place to better understand how customers interact with your business. 

    Easy, custom analysis with SQL-like queries 

    Standard, pre-built reports often don’t address the specific, detailed questions that analysts need to answer.  

    When you use the BigQuery & Data Warehouse Export feature, you can use SQL-like queries in your data warehouse to do detailed, customised analysis. This flexibility allows you to explore your data in depth and uncover specific insights that aren’t possible with pre-built reports. 

    Here is an example of how you might use SQL-like query to compare the behaviours of paying vs. non-paying users : 

    				
                                            <xmp>SELECT  

    custom_dimension_value AS user_type, -- Assuming 'user_type' is stored in a custom dimension

    COUNT(*) AS total_visits,  

    AVG(visit_total_time) AS avg_duration,

    SUM(conversion.revenue) AS total_spent  

    FROM  

    `your_project.your_dataset.matomo_log_visit` AS visit

    LEFT JOIN  

    `your_project.your_dataset.matomo_log_conversion` AS conversion  

    ON  

    visit.idvisit = conversion.idvisit  

    GROUP BY  

    custom_dimension_value; </xmp>
                                   

    This query helps you compare metrics such as the number of visits, average session duration, and total amount spent between paying and non-paying users. It provides a full view of behavioural differences between these groups. 

    Advanced data manipulation and visualisation 

    When you need to create detailed reports or dive deep into data analysis, working within the constraints of a fixed user interface (UI) can limit your ability to draw insights. 

    Exporting your Matomo data to a data warehouse like BigQuery provides greater flexibility for in-depth manipulation and advanced visualisations, enabling you to uncover deeper insights and tailor your reports more effectively. 

    Getting started 

    To set up data warehouse exports in your Matomo : 

    1. Go to System Admin (cog icon in the top right corner) 
    2. Select ‘Export’ from the left-hand menu 
    3. Choose ‘BigQuery & Data Warehouse’ 

    You’ll find detailed instructions in our data warehouse exports guide 

    Please note, enabling this feature will cost an additional 10% of your current subscription. You can view the exact cost by following the steps above. 

    New to Matomo ? Start your 21-day free trial now (no credit card required), or request a demo.