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    La gestion de la ferme passe par l’exécution à intervalle régulier de plusieurs tâches répétitives dites Cron.
    Le super Cron (gestion_mutu_super_cron)
    Cette tâche, planifiée chaque minute, a pour simple effet d’appeler le Cron de l’ensemble des instances de la mutualisation régulièrement. Couplée avec un Cron système sur le site central de la mutualisation, cela permet de simplement générer des visites régulières sur les différents sites et éviter que les tâches des sites peu visités soient trop (...)

  • Personnaliser les catégories

    21 juin 2013, par

    Formulaire de création d’une catégorie
    Pour ceux qui connaissent bien SPIP, une catégorie peut être assimilée à une rubrique.
    Dans le cas d’un document de type catégorie, les champs proposés par défaut sont : Texte
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    Administration > Configuration des masques de formulaire.
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  • Emballe médias : à quoi cela sert ?

    4 février 2011, par

    Ce plugin vise à gérer des sites de mise en ligne de documents de tous types.
    Il crée des "médias", à savoir : un "média" est un article au sens SPIP créé automatiquement lors du téléversement d’un document qu’il soit audio, vidéo, image ou textuel ; un seul document ne peut être lié à un article dit "média" ;

Sur d’autres sites (6137)

  • Use FFmpeg concat two video, is output video level mistake ?

    27 février, par 哇哈哈
    video1
{
    "index": 0,
    "codec_name": "hevc",
    "codec_long_name": "H.265 / HEVC (High Efficiency Video Coding)",
    "profile": "Main",
    "codec_type": "video",
    "codec_tag_string": "hev1",
    "codec_tag": "0x31766568",
    "width": 1920,
    "height": 1080,
    "coded_width": 1920,
    "coded_height": 1080,
    "has_b_frames": 2,
    "sample_aspect_ratio": "1:1",
    "display_aspect_ratio": "16:9",
    "pix_fmt": "yuv420p",
    "level": 120,
    "color_range": "tv",
    "chroma_location": "left",
    "field_order": "progressive",
    "refs": 1,
    "view_ids_available": "",
    "view_pos_available": "",
    "id": "0x1",
    "r_frame_rate": "30/1",
    "avg_frame_rate": "30/1",
    "time_base": "1/15360",
    "start_pts": 0,
    "start_time": "0.000000",
    "duration_ts": 200192,
    "duration": "13.033333",
    "bit_rate": "10794613",
    "nb_frames": "391",
    "extradata_size": 2496,
    "disposition": {
        "default": 1,
        "dub": 0,
        "original": 0,
        "comment": 0,
        "lyrics": 0,
        "karaoke": 0,
        "forced": 0,
        "hearing_impaired": 0,
        "visual_impaired": 0,
        "clean_effects": 0,
        "attached_pic": 0,
        "timed_thumbnails": 0,
        "non_diegetic": 0,
        "captions": 0,
        "descriptions": 0,
        "metadata": 0,
        "dependent": 0,
        "still_image": 0,
        "multilayer": 0
    },
    "tags": {
        "language": "eng",
        "handler_name": "VideoHandler",
        "vendor_id": "[0][0][0][0]",
        "encoder": "Lavc61.33.100 libx265",
        "timecode": "00:00:00;00"
    }
}

video2 
{
    "index": 0,
    "codec_name": "hevc",
    "codec_long_name": "H.265 / HEVC (High Efficiency Video Coding)",
    "profile": "Main",
    "codec_type": "video",
    "codec_tag_string": "hev1",
    "codec_tag": "0x31766568",
    "width": 1920,
    "height": 1080,
    "coded_width": 1920,
    "coded_height": 1080,
    "has_b_frames": 2,
    "sample_aspect_ratio": "1:1",
    "display_aspect_ratio": "16:9",
    "pix_fmt": "yuv420p",
    "level": 120,
    "color_range": "tv",
    "chroma_location": "left",
    "field_order": "progressive",
    "refs": 1,
    "view_ids_available": "",
    "view_pos_available": "",
    "id": "0x1",
    "r_frame_rate": "25/1",
    "avg_frame_rate": "25/1",
    "time_base": "1/12800",
    "start_pts": 0,
    "start_time": "0.000000",
    "duration_ts": 1309696,
    "duration": "102.320000",
    "bit_rate": "1024122",
    "nb_frames": "2558",
    "extradata_size": 2496,
    "disposition": {
        "default": 1,
        "dub": 0,
        "original": 0,
        "comment": 0,
        "lyrics": 0,
        "karaoke": 0,
        "forced": 0,
        "hearing_impaired": 0,
        "visual_impaired": 0,
        "clean_effects": 0,
        "attached_pic": 0,
        "timed_thumbnails": 0,
        "non_diegetic": 0,
        "captions": 0,
        "descriptions": 0,
        "metadata": 0,
        "dependent": 0,
        "still_image": 0,
        "multilayer": 0
    },
    "tags": {
        "language": "und",
        "handler_name": "VideoHandler",
        "vendor_id": "[0][0][0][0]",
        "encoder": "Lavc61.33.100 libx265"
    }
}

out:
{
    "index": 0,
    "codec_name": "hevc",
    "codec_long_name": "H.265 / HEVC (High Efficiency Video Coding)",
    "profile": "Main",
    "codec_type": "video",
    "codec_tag_string": "hev1",
    "codec_tag": "0x31766568",
    "width": 1920,
    "height": 1080,
    "coded_width": 1920,
    "coded_height": 1080,
    "has_b_frames": 2,
    "sample_aspect_ratio": "1:1",
    "display_aspect_ratio": "16:9",
    "pix_fmt": "yuv420p",
    "level": 186,
    "color_range": "tv",
    "chroma_location": "left",
    "field_order": "progressive",
    "refs": 1,
    "view_ids_available": "",
    "view_pos_available": "",
    "id": "0x1",
    "r_frame_rate": "30/1",
    "avg_frame_rate": "147450/5767",
    "time_base": "1/1000000",
    "start_pts": 0,
    "start_time": "0.000000",
    "duration_ts": 115340000,
    "duration": "115.340000",
    "bit_rate": "1060604",
    "nb_frames": "2949",
    "extradata_size": 2500,
    "disposition": {
        "default": 1,
        "dub": 0,
        "original": 0,
        "comment": 0,
        "lyrics": 0,
        "karaoke": 0,
        "forced": 0,
        "hearing_impaired": 0,
        "visual_impaired": 0,
        "clean_effects": 0,
        "attached_pic": 0,
        "timed_thumbnails": 0,
        "non_diegetic": 0,
        "captions": 0,
        "descriptions": 0,
        "metadata": 0,
        "dependent": 0,
        "still_image": 0,
        "multilayer": 0
    },
    "tags": {
        "language": "und",
        "handler_name": "VideoHandler",
        "vendor_id": "[0][0][0][0]",
        "encoder": "Lavc61.33.100 libx265"
    }
}


    


    output video level is 6.2 ? i wiki level refer to fps resolusion or bitrate,but not suit this output video.
0。0 ! Could Someone HELP me ?

    


    ffmpeg -i .\HEVC_1080p_30P_yellowtree.mp4 -i .\HEVC_1080p_24fps_happy.mp4 -filter_complex "[0:v][1:v]concat=n=2:v=1:a=0[outv]" -map "[outv]" -c:v libx265 concat_output.mp4

    


    ffmpeg version N-118448-g43be8d0728-20250209 Copyright (c) 2000-2025 the FFmpeg developers
built with gcc 14.2.0 (crosstool-NG 1.26.0.120_4d36f27)
configuration : —prefix=/ffbuild/prefix —pkg-config-flags=—static —pkg-config=pkg-config —cross-prefix=x86_64-w64-mingw32- —arch=x86_64 —target-os=mingw32 —enable-gpl —enable-version3 —disable-debug —enable-shared —disable-static —disable-w32threads —enable-pthreads —enable-iconv —enable-zlib —enable-libfreetype —enable-libfribidi —enable-gmp —enable-libxml2 —enable-lzma —enable-fontconfig —enable-libharfbuzz —enable-libvorbis —enable-opencl —disable-libpulse —enable-libvmaf —disable-libxcb —disable-xlib —enable-amf —enable-libaom —enable-libaribb24 —enable-avisynth —enable-chromaprint —enable-libdav1d —enable-libdavs2 —enable-libdvdread —enable-libdvdnav —disable-libfdk-aac —enable-ffnvcodec —enable-cuda-llvm —enable-frei0r —enable-libgme —enable-libkvazaar —enable-libaribcaption —enable-libass —enable-libbluray —enable-libjxl —enable-libmp3lame —enable-libopus —enable-librist —enable-libssh —enable-libtheora —enable-libvpx —enable-libwebp —enable-libzmq —enable-lv2 —enable-libvpl —enable-openal —enable-libopencore-amrnb —enable-libopencore-amrwb —enable-libopenh264 —enable-libopenjpeg —enable-libopenmpt —enable-librav1e —enable-librubberband —enable-schannel —enable-sdl2 —enable-libsnappy —enable-libsoxr —enable-libsrt —enable-libsvtav1 —enable-libtwolame —enable-libuavs3d —disable-libdrm —enable-vaapi —enable-libvidstab —enable-vulkan —enable-libshaderc —enable-libplacebo —disable-libvvenc —enable-libx264 —enable-libx265 —enable-libxavs2 —enable-libxvid —enable-libzimg —enable-libzvbi —extra-cflags=-DLIBTWOLAME_STATIC —extra-cxxflags= —extra-libs=-lgomp —extra-ldflags=-pthread —extra-ldexeflags= —cc=x86_64-w64-mingw32-gcc —cxx=x86_64-w64-mingw32-g++ —ar=x86_64-w64-mingw32-gcc-ar —ranlib=x86_64-w64-mingw32-gcc-ranlib —nm=x86_64-w64-mingw32-gcc-nm —extra-version=20250209
libavutil 59. 56.100 / 59. 56.100
libavcodec 61. 33.100 / 61. 33.100
libavformat 61. 9.107 / 61. 9.107
libavdevice 61. 4.100 / 61. 4.100
libavfilter 10. 9.100 / 10. 9.100
libswscale 8. 13.100 / 8. 13.100
libswresample 5. 4.100 / 5. 4.100
libpostproc 58. 4.100 / 58. 4.100

    


  • Introducing the Data Warehouse Connector 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 Data Warehouse Connector 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 Data Warehouse Connector 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 Data Warehouse Connector 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 Data Warehouse Connector 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 ‘Data Warehouse Connector’ 

    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. 

  • How to grab laptop webcam video with ffmpeg in windows

    25 août 2018, par Ofer Sadan

    I have a small python program that works very well to capture short videos from webcams in linux (at least for laptops that have built-in webcams) using a sub-process with ffmpeg.

    Now i’m trying to write the same program to capture webcams in windows, and i know i can’t use the generic "/dev/video0" that works pretty well in linux, but i thought something like naming it "Integrated Camera" should be enough, but it fails.

    Here’s my linux code (that works) :

        import sys
    
    from subprocess import call
    
    from datetime import datetime
    
    def record_webcam(seconds):
    
        cam = '/dev/video0'
    
        timestamp = datetime.now().strftime('%Y%m%d-%H%M%S')
    
        filename = timestamp + 'something.mkv' #generated with more complexity in the actual code, but that isn't important
    
        ffmpeg_cmd = 'ffmpeg -t {} -an -i {} -c:v libx264 -preset veryslow -crf 25 {}'.format(seconds, cam, filename).split()
    
        p = call(ffmpeg_cmd)
    
        return filename if p == 0 else False
    

    
    if __name__ == '__main__':
    
        record_webcam(sys.argv[1])

    I have looked at the documentation for ffmpeg and tried to search for solution but so far i’m lost...

    I know that "Integrated Camera"s are only available on some laptops and not others, and that it won’t capture other cameras connected, but it’s enough for my use case... but if you want a challenge I would also like to know how to apply it to any windows-pc with a camera regardless of what it’s called.

    Also, is it easier or more recommended to do what i’m trying here only with python tools, like OpenCV ?

    Thanks in advance !
    Edit : I answered my own question with a partial solution if anyone is interested based on a comment from @Mulvya, but if anyone can still explain to me the part about OpenCV I would still like to hear it...

    Follow up question here : ffmpeg through python subprocess fails to find camera

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