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• Unwrapping Matomo 5.2.0 – Bringing you enhanced security and performance

  25 décembre 2024, par Daniel Crough — Latest Releases

   As we tie a bow on 2024, we’re delighted to share our final gift of the year. Matomo 5.2.0 comes wrapped with new security features, privacy controls, and performance improvements to enhance your analytics experience.

   Enhanced security and privacy controls

  

  We’ve strengthened Matomo’s security framework with several key updates :

  • A new installer timestamp mechanism for on-premise installations creates a secure 72-hour installation window, preventing unauthorised access during setup
  • Enhanced account security features including a “This Wasn’t Me” link in password reset emails and location-based login alerts
  • The new Global List of Query URL parameters feature lets you refine tracking by excluding sensitive or unnecessary parameters from collection

  Tag manager improvements for better efficiency

  

  The Matomo Tag Manager now includes several features to streamline your workflow :

  • New Consent Management Platform (CMP) tags for CookieYes, OneTrust, and Axeptio, simplifying consent tracking implementatio.
  • A new copy feature for containers, tags, and triggers that reduces setup time and ensures consistency across multiple properties
  • Improved management tools for maintaining standardised tracking across websites

  Performance and reliability updates

  We’ve made technical improvements to enhance Matomo’s performance :

  • Full PHP 8.4 compatibility for improved performance and reliability
  • Optimised archiving processes including smarter handling of recent data
  • New recovery options for interrupted archiving processes
  • Specific improvements for TiDB compatibility, enhancing database performance for users of this engine

  Important to note : This release does not require any major database upgrade, making it easier to implement these improvements.

  Looking forward to 2025

  As we prepare to enter a new year, these updates reflect our ongoing commitment to providing privacy-focused analytics. We’re grateful to all our community contributors who have helped make this release possible. Special thanks to the Matomo community for their contributions to this release.

  Ready to explore these new features ? Update to Matomo 5.2.0 today and start the new year with enhanced security, efficiency, and control over your analytics data.

  From all of us at Matomo, thank you for being part of our journey. Here’s to another year of protecting privacy and empowering insights together !

  ---

  For a detailed overview of all changes and improvements, see our complete release notes or join the discussion in our community forums. If you’d like to contribute to making Matomo even better, learn more about getting involved with our open-source project.

• Muxing in audio to gstreamer RTMP stream kills both video and Audio

  1er avril 2015, par Adam

  I need some genius help here - I’m trying to set up a live stream for my upcoming wedding... and I have it ALMOST working - audio seems to be the problem.

  This is my setup

  • Raspberry Pi Model B+
  • Logitech C920 (with onboard h264 encoding that I am utilising)
  • on-camera (C920) microphone
  • USB wifi to iPhone 4G connection
  • gstreamer1.0
  • Amazon EC2 Wowza RTMP server

  I have it all set up, but as soon as I mux in the audio, the streams wont play by any player.

  What Works :
  - my gstreamer pipeline WITHOUT the audio muxed in
  - Wowza receives a consistent stream, no failures
  - The various Flash players / iOS / Android and VLC all play back the video

  What doesnt :
  - enabling audio in the mux (using the pipeline below)
  - BUT gstreamer doesnt complain
  - BUT Wowza receives a consistent stream, no failures
  - The various flash players fail to play both Audio and Video. some just display the first video frame
  - VLC plays 1 video frame, and about 100ms of audio, then stops

  Ideally I’d like the muxed audio/video FLV stored on the SD card too in case the network goes down - but if the ’tee’ needs to be sacrificed to make it work, so be it.

  This is my current FAILING pipeline - I assume there’s something really stupid in it because I know practically nothing about gstreamer.... The first frame loads in all the players (except iOS.. which never shows anything)

  # set camera resolution to 720p, and the data format to H264 (alternatives are YUV and JPG)
  
  v4l2-ctl --device=/dev/video0 --set-fmt-video=width=1280,height=720,pixelformat=1
  
  # set the frame rate
  
  v4l2-ctl --device=/dev/video0 --set-parm=10
  
  
  
  gst-launch-1.0 -v -e uvch264src initial-bitrate=300000 average-bitrate=300000 device=/dev/video0 name=src auto-start=true src.vidsrc \
  
                  ! queue \
  
                  ! video/x-h264,width=1280,height=720,framerate=10/1 \
  
                  ! h264parse \
  
                  ! flvmux streamable=true name=mux \
  
                  ! queue \
  
                  ! tee name=t \
  
                  ! queue \
  
                  ! filesink location=/home/pi/wedding.flv t. \
  
                  ! queue \
  
                  ! rtmpsink location='rtmp://wowzaserver/live/wedding live=1' >>/home/pi/wedding.log 2>&1

  Some of the things I can’t really afford to change at this late stage are the encapsulation (FLV) and wowza RTMP because I’ve built everything around that...

  Please Help !! Thanks !

  UPDATE

  Given that I am also saving the FLV file, I have found that if I use ffmpeg to send that FLV file (using audio copy, video copy) to the RTMP server, everything works (but obviously its not live) ! So I am now starting to believe this is a problem with the way Gstreamer encapsulates RTMP - and by putting ffmpeg in the middle it fixes it... but it’s not live of course.
  Is it possible to pipe my output to ffmpeg and using ffmpeg’s RTMP ?

• How to combine software and hardware video filters in FFmpeg, e.g. using CUDA/nvenc ? [closed]

  3 novembre 2020, par bemo

  One function of my Windows software project requires capturing video at 100fps for 10 seconds simultaneously from 4 machine vision cameras. I'm initially storing the video data in uncompressed/raw video files, one per camera, where these files are just concatenated raw frame data from the camera (in Bayer rggb8 format).

  


  After capturing, I need to convert these into a compressed format (I don't do this at the time of capture to avoid clogging up the CPU and minimise the risk of dropping frames). I'm using ffmpeg for this and I've arrived at this command that gives satisfactory results :

  


  ffmpeg.exe -y -an -f rawvideo -pixel_format bayer_rggb8 -video_size 2048x1536 -framerate 100 -i input.raw -codec:v libx264 -preset medium -crf 17 -vf "vignette=mode=backward:a=PI/7,lenscorrection=k1=-0.14,unsharp=13:13:1.5:5:5:0,hqdn3d,format=yuv420p" output.mp4

  


  Since the above uses software encoding it is pretty slow and takes several minutes to complete the 4x10s videos. I need it to be a lot faster to avoid the user having to wait too long within my app for transcoding to finish. So, I want to speed this up using ffmpeg's hardware acceleration.

  


  I've purchased a nvenc/CUDA-compatible GeForce card, have compiled ffmpeg with CUDA support and have experimented with the h264_nvenc encoder. Basic encoding works (without encoding parameters and filters) but I've got two specific problems :

  


  

  1. -crf (and possibly -present) aren't supported by h264_nvenc, so what is the hardware-accelerated equivalent to -preset medium -crf 17 ?

  


  2. How can I apply filters to reduce vignetting, correct lens distortion (2 cameras are using fish-eye lenses), sharpen the images and then de-noise them, whilst still significantly improving overall encoding speed ? I understand some filters aren't supported by hardware so I need to use hwupload_cuda and hwdownload to move data between video memory and main memory, but I can't get the exact encoding incantation working and I'm also worried I'll lose all the hardware-acceleration benefit if I have to do too much in software.

  


  


  I'm open to using alternatives to nvenc if necessary - e.g. VAAPI, libmfx/QuickSync or OpenCL as mentioned on ffmpeg's hardware acceleration page.