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• v4l2loopback+ffmpeg input for uvc gadget

  13 mai, par Mosi

  I'm trying to use an MP4 video file as the input for a UVC Gadget setup on my Raspberry Pi 4 Model B, but I'm running into an issue when streaming through V4L2.

  


  Goal :

  


  To emulate a webcam that streams a looping MP4 video file to a Windows 11 host.

  


  My setup :

  


  

  • Hardware : Raspberry Pi 4 Model B

  


  • OS : Raspberry Pi OS Lite 64-bit (2025-05-06-raspios-bookworm-arm64-lite)

  


  • Kernel : 6.12.25+rpt-rpi-v8

  


  • Host System : Windows 11

  


  • UVC Gadget version : v0.3.0

  


  


  Workflow :

  


  [MP4 Video] → [FFmpeg] → [V4L2 Loopback] → [UVC Gadget] → Windows sees virtual webcam


  


  What works :

  


  The UVC Gadget works perfectly when I use a real webcam as the source (e.g., /dev/video0). Windows detects the virtual webcam and displays a smooth video stream.

  


  The problem :

  


  When I try to use an MP4 video file through FFmpeg and send it to the loopback device (/dev/video3), the UVC Gadget fails with the following error :

  


  ---

  


  Command I'm using :

  


  ffmpeg -re -stream_loop -1 -i input.mp4 -vf scale=640:480 \
  -c:v rawvideo -pix_fmt yuyv422 -r 30 -f v4l2 /dev/video3


  


  Then I run :

  


  sudo uvc-gadget -d /dev/video3 uvc.0


  


  Output :

  


  bRequestType 21 bRequest 01 wValue 0200 wIndex 0001 wLength 0022
streaming request (req SET_CUR cs 02)
setting commit control, length = 34
Setting format to 0x56595559 640x480
=== Setting frame rate to 30 fps
Starting video stream.
--> [At this point I open the camera on the Windows host]
/dev/video3: 2 buffers requested.
Failed to export buffer 0.
Failed to export buffers on source: Inappropriate ioctl for device (25)


  


  ---

  


  Things I've tried :

  


  

  • Multiple FFmpeg formats, resolutions, and pixel formats

  


  • Various ffmpeg buffer and framerate tweaks

  


  • Different UVC Gadget versions

  


  • GitHub related projects (showcamera, etc.)

  


  • Older Raspberry Pi OS versions

  


  


  Most guides and GitHub projects I found are outdated (5+ years old), and newer methods seem undocumented or incompatible with current kernel/UVC gadget tools.

  


  ---

  


  My question :

  


  How can I stream an MP4 file as a virtual webcam using UVC Gadget without getting ioctl errors ?
  
Is there a proper way to set up FFmpeg and loopback devices so that UVC Gadget can read the stream correctly ?

  


  Any modern working example or tips would be very appreciated. Thanks in advance !

  


• 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 ?

  


• Enhanced Privacy Control : Matomo’s Guide for Consent Manager Platform Integrations

  13 février, par Alex Carmona — Development, Latest Releases

  In today’s digital landscape, protecting user privacy isn’t just about compliance—it’s about building trust and demonstrating respect for user choices. Even though you can use Matomo without requiring consent when properly configured in compliance with privacy regulations, we’re excited to introduce a new Consent Manager Platforms (CMP) category on our Integrations page to make it easier than ever to implement privacy-respecting analytics.

  What’s a consent manager platform ?

  A Consent Management Platform (CMP) is a tool that helps websites collect, manage, and store user consent for data tracking and cookies in compliance with privacy regulations like GDPR and CCPA. A CMP allows users to choose which types of data they want to share, ensuring transparency and respecting their privacy preferences. By integrating a CMP with Matomo, organisations can make sure that analytics tracking occurs only after obtaining explicit user consent.

  

  Remember, you can configure Matomo to remain fully GDPR compliant, without requiring user consent.

  Why consent management matters

  With privacy regulations reshaping data collection practices daily, organisations need to ensure that analytics data is gathered only after users have explicitly given their consent. Integrating Matomo with a Consent Management Platform helps you :

  • Strengthen regulatory compliance
  • Enhance user trust through transparency
  • Clearly document consent choices
  • Simplify privacy management

  By making consent management seamless, you can maintain compliance while delivering a privacy-first experience to your users.

  Introducing our CMP integration options

  We’ve carefully curated integrations with leading Consent Management Platforms that work seamlessly with Matomo Analytics and Matomo Tag Manager. Our supported platforms include :

  

  Supported consent management platforms

  • Osano – Comprehensive consent management with global regulation support
  • Cookiebot – Advanced cookie consent and compliance automation
  • CookieYes – User-friendly consent management solution
  • Tarte au Citron – Open-source consent management tool
  • Klaro – Privacy-focused consent management system
  • OneTrust – Enterprise-grade privacy management platform
  • Complianz for WordPress – Specialised WordPress consent solution

  Each platform provides unique features and compliance options, allowing you to select the best fit for your privacy needs.

  Getting started with simplified implementation

  Ready to enhance your privacy compliance ? We’ve made the integration process straightforward, so you can set up a privacy-compliant analytics environment in just a few steps. Here’s how to begin :

  1. Explore our new CMP category on the Integrations page
  2. Select and implement the CMP that best suits your needs
  3. Check our implementation guides for step-by-step instructions
  4. Configure your consent management settings in Matomo
  5. Start collecting analytics data with proper consent management

  Moving Forward

  As privacy regulations evolve and user expectations around data protection grow, proper consent management is more important than ever. With Matomo’s new CMP integrations, you can ensure compliance while maintaining full control over your analytics data.

  Visit our Integrations page and our Implementation guides today to explore these privacy-enhancing solutions and take the next step in your privacy-first analytics journey.