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• ffmpeg is not capturing udp output

  30 octobre 2020, par Dzidas

  I'm trying to capture UDP output with ffmpeg, but the stream looks empty. Just for background, the output is produced by GoPro camera and I'm connecting to its Wifi. I can see with wireshark a lot of udp packets sent to my ip from GoPro IP, but nothing appears in ffmpeg.

  


  ffplay -fflags nobuffer -f:v mpegts -probesize 8192 udp://10.5.5.9:8554
ffplay version 4.3.1 Copyright (c) 2003-2020 the FFmpeg developers
built with Apple clang version 11.0.0 (clang-1100.0.33.17)
configuration: --prefix=/usr/local/Cellar/ffmpeg/4.3.1_2 --enable-shared --enable-pthreads --enable-version3 --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-librtmp --enable-libspeex --enable-libsoxr --enable-videotoolbox --disable-libjack --disable-indev=jack
libavutil      56. 51.100 / 56. 51.100
libavcodec     58. 91.100 / 58. 91.100
libavformat    58. 45.100 / 58. 45.100
libavdevice    58. 10.100 / 58. 10.100
libavfilter     7. 85.100 /  7. 85.100
libavresample   4.  0.  0 /  4.  0.  0
libswscale      5.  7.100 /  5.  7.100
libswresample   3.  7.100 /  3.  7.100
libpostproc    55.  7.100 / 55.  7.100
nan    :  0.000 fd=   0 aq=    0KB vq=    0KB sq=    0B f=0/0


  


  Any idea or suggestion how could I debug or understand the cause ?

  


  Thanks !

  


• How to use Behavioural Analytics to Improve Website Performance

  20 septembre 2021, par Ben Erskine — Analytics Tips, Plugins, Heatmap
  

  User behavioural analytics (UBA) give your business unique insights into your customers. 

  Where traditional website metrics track what actions are completed or how many visitors you have, user behaviour shows the driving factors behind those actions. UBA tools such as website heatmap software provide an easy-to-read visualisation of this data. 

  Ultimately, user behaviour analysis improves website performance and conversions by boosting customer engagement, optimising positive customer experiences, and focusing on the most important part of your sales : the people who are actually buying from you. 

  What is user behaviour analytics ?

  User behaviour analytics (UBA) is data that shows how customers and website visitors interact with your brand online. 

  UBA is tracked using tools such as heatmaps, session recordings and data visualisation software. 

  Where traditional web analytics track metrics such as page views and bounce rates, behavioural analytics provide an even more in-depth picture of your website or funnel success. 

  For example, UBA tracks actions like 

  • How far users are scrolling down the page 
  • Which CTA’s and copy they are focusing on (or not focusing on) 
  • Which design elements, links or buttons they are interacting with 
  • What is happening in between each action

  Tracking user behaviour metrics help keep visitors on your website longer because they analyse where customers may be confused or unclear so you can fix it. 

  What’s the difference between data and behavioural analytics ?

  There are a few key differences between data and behavioural analytics. While data analytics are beneficial to improving website performance, using UBA creates a more customer-centric approach to funnel building. 

  The biggest difference between data and behavioural analytics ? Metric data shows which actions are happening. Behavioural analytics show you WHY they are happening. 

  For example, data can show you that a customer bounced or clicked away. Behaviour analytics show you that a page took a long time to load, they tried to click a link several times and then maybe got frustrated and clicked away. 

  Key differences between data analytics and behavioural analytics : 

  • What is happening versus what is driving it 
  • Track an action (e.g. click-through) versus tracking inaction (e.g. hover without clicking) 
  • Measuring completion of an action versus the flow of actions to complete action 
  • Source of traffic versus individual actions 
  • What happens when someone takes an action versus what happens in between taking action 

  Matomo heatmaps offer both website analytics and user behaviour for a comprehensive analysis.

  Why do behavioural analytics help improve website performance ?

  User behaviour is important because it doesn’t matter how many website visitors you have if they don’t convert. 

  If you have a lot of traffic on mobile devices, but a low CTR, heatmaps show you what is causing the low conversions. Perhaps there is a button that isn’t optimised for mobile scrolling, or a pop up that covers important copy. 

  Analysing the driving factors behind each decision means that you can increase sign-ups and conversions without losing money on website traffic that never actually buys. 

  

  How do heatmap tools show website user behaviour analytics ? 

  Heatmap tools provide a visual representation of user behaviour. 

  There are several key ways that heatmap tracking can improve website performance and therefore your overall conversions.

  Firstly, heatmaps show where to optimise website structure. It uses real visitor experiences to indicate whether customers have to scroll to reach important content, whether important messages are being missed, and whether CTAs are clear. 

  Secondly, heatmaps provide always-on UX and useability testing for your website, identifying user frustrations and optimising their experience over time.

  They also show valuable user experience insights for A/B versions of a landing page. Not only will you see the raw conversion data, but you will also understand why one page converts more than another.

  Ultimately, heatmaps increase ROI on marketing by optimising the traffic that you are sending to your website.

  

  5 ways heatmaps and user behaviour analytics improve website performance and conversions

  #1. Improve customer experience

  One of the most important uses for UBA is to improve your customer experience. 

  Imagine you had a physical store. If there was something blocking customers from getting to the counter you could easily see and fix the problem. 

  It is just as important for an online store to find and fix these “roadblocks”. 

  Not only does it reduce friction in the sales funnel and make it easy for customers to buy from you, it improves their overall experience. And when 86% of buyers are willing to pay more for a great customer experience, UBA should be one of your number one priorities for growing your bottom line. 

  #2. Improve customer engagement

  Customer engagement is any interaction between a customer/product user and your business. 

  User behaviour analytics increase engagement at each customer journey touch point. 

  Using data from heatmaps will improve customer engagement because it gives you insights into how you can make your website more user friendly. This reduces friction and increases customer loyalty by making sure customers :

  • See important content 
  • Are not distracted by unnecessary elements 
  • Can easily access information or pages no matter what device they are using 
  • Are clicking on important page elements that take them further through the customer journey 

  For example, say a customer is on a sales page. A heatmap might show that pop ups or design elements like links to another page are pulling their attention away from the primary focus (i.e. the sales copy). 

  #3. Focus on customer-centric approach 

  A customer-centric approach means putting your customers at the centre of everything that you do. There is a lot of competition for your customers’ hard earned dollars, so you need to stand out. A good product or service is not enough on its own anymore. 

  User behaviour analytics are at the heart of customer-centric strategies. Instead of guessing how customers interact with your online presence, tools like heatmaps give insight into exactly what customers need. 

  This matched with an effective customer feedback strategy gives a holistic and effective approach to improving your customer experiences. 
  

  #4. Capture customer data across multiple channels

  Most customers won’t convert on their very first visit to a website. They might interact with your business across many channels and research your product multiple times before purchasing. 

  Multi Channel Conversion Attribution, also known as Cross Channel Attribution, lets you assign a value to each visit prior to a conversion or prior to a sale. By applying different attribution models, you get a better view on which channels actually lead to a conversion.

  User behaviour analytics like the multi channel conversion attribution that Matomo offers can show you exactly where you should focus your money to acquire new customers. 

  #5. Track and measure business objectives

  User behaviour analytics like heatmaps can show you whether you are actually hitting your targets. 

  Setting goals helps track your website performance against business objectives. 

  These include objectives such as lead generation, online sales and increased brand exposure. Matomo has a specific function for tracking goals and measuring analytics.

  Using a combination of UBA and data metrics will produce the most effective conversions. 

  For example, a customer reaching the payment confirmation page is a common objective to measure conversions. However, it is only tracked if they actually complete the action. Measuring on-page customer activity with heatmaps shows why they do or do not convert so you can fix issues. 

  Final thoughts on user behaviour analytics 

  User behavioural analytics (UBA) provide a unique and in-depth insight into your customers and their needs. Unlike traditional data metrics that track completed actions, UBA like heatmaps show you what happens in between each action and help fix any critical issues. 

  Heatmaps are your secret weapon to improving website performance while staying customer-centric ! 

  Want to know how heatmap analytics increase conversions and improve customer experience without spending more on traffic or marketing ? Check out some of the other in depth guides below. 

  The Ultimate Guide to Heatmap Software

  10 Proven Ways Heatmap Software Improves Website Conversions

  Heatmap Video

  Session Recording Video

• Looking for a general purpose way to merge audio and video file and make an output have stereo audio with FFmpeg

  21 février 2020, par Pineapple Joe

  So have have the following FFmpeg command

  ffmpeg -i vidab.mp4 -i recab.webm  -filter_complex "[0:a]volume=0.2,apad[A];[1:a][A]amerge[Aout]" -map 0:v -map [Aout] -y mergeab.mp4

  When the input files are played separately their audio is played equally through both headphones.

  But after being merged the audio from the video is on the right side and the audio from the webm file is on the left side.

  I see this in the output when I run the cmd

  [Parsed_amerge_2 @ 0x7fa0faf01bc0] No channel layout for input 1
  
  [Parsed_amerge_2 @ 0x7fa0faf01bc0] Input channel layouts overlap: output layout will be determined by the number of distinct input channels

  How can I adjust this so that the output file has the audio from both the inputs in equally in the left and right sides ?

  Also is there a generic way to do this such that even different configurations (video has stereo and audio has mono ... or both have stereo) result in the Audio being split evenly.

  I tried using the pan filter but there is something I am not understanding about how it works because either nothing happens or I flip left and right. But I am not getting even stereo.

  fwiw here is the full output.

  ffmpeg -i vidab.mp4 -i recab.webm  -filter_complex "[0:a]volume=0.2,apad[A];[1:a][A]amerge[Aout]" -map 0:v -map [Aout] -y mergeab.mp4
  
  ffmpeg version 4.2.1 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with Apple clang version 11.0.0 (clang-1100.0.33.8)
  
    configuration: --prefix=/usr/local/Cellar/ffmpeg/4.2.1_2 --enable-shared --enable-pthreads --enable-version3 --enable-avresample --cc=clang --host-cflags='-I/Library/Java/JavaVirtualMachines/adoptopenjdk-13.jdk/Contents/Home/include -I/Library/Java/JavaVirtualMachines/adoptopenjdk-13.jdk/Contents/Home/include/darwin -fno-stack-check' --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libmp3lame --enable-libopus --enable-librubberband --enable-libsnappy --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-librtmp --enable-libspeex --enable-libsoxr --enable-videotoolbox --disable-libjack --disable-indev=jack
  
    libavutil      56. 31.100 / 56. 31.100
  
    libavcodec     58. 54.100 / 58. 54.100
  
    libavformat    58. 29.100 / 58. 29.100
  
    libavdevice    58.  8.100 / 58.  8.100
  
    libavfilter     7. 57.100 /  7. 57.100
  
    libavresample   4.  0.  0 /  4.  0.  0
  
    libswscale      5.  5.100 /  5.  5.100
  
    libswresample   3.  5.100 /  3.  5.100
  
    libpostproc    55.  5.100 / 55.  5.100
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'vidab.mp4':
  
    Metadata:
  
      major_brand     : qt  
  
      minor_version   : 0
  
      compatible_brands: qt  
  
      creation_time   : 2020-02-21T00:35:25.000000Z
  
    Duration: 00:00:10.65, start: 0.000000, bitrate: 2539 kb/s
  
      Stream #0:0(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, mono, fltp, 96 kb/s (default)
  
      Metadata:
  
        creation_time   : 2020-02-21T00:35:25.000000Z
  
        handler_name    : Core Media Audio
  
      Stream #0:1(und): Video: h264 (High) (avc1 / 0x31637661), yuvj420p(pc, bt709), 1280x640, 2436 kb/s, 29.87 fps, 29.97 tbr, 90k tbn, 180k tbc (default)
  
      Metadata:
  
        creation_time   : 2020-02-21T00:35:25.000000Z
  
        handler_name    : Core Media Video
  
        encoder         : H.264
  
  Input #1, matroska,webm, from 'recab.webm':
  
    Metadata:
  
      encoder         : opus-media-recorder
  
    Duration: N/A, start: 0.000000, bitrate: N/A
  
      Stream #1:0(eng): Audio: opus, 48000 Hz, mono, fltp (default)
  
  Stream mapping:
  
    Stream #0:0 (aac) -> volume (graph 0)
  
    Stream #1:0 (opus) -> amerge:in0 (graph 0)
  
    Stream #0:1 -> #0:0 (h264 (native) -> h264 (libx264))
  
    amerge (graph 0) -> Stream #0:1 (aac)
  
  Press [q] to stop, [?] for help
  
  [libx264 @ 0x7fa0fb004000] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
  
  [libx264 @ 0x7fa0fb004000] profile High, level 3.1
  
  [libx264 @ 0x7fa0fb004000] 264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
  
  [Parsed_amerge_2 @ 0x7fa0faf01bc0] No channel layout for input 1
  
  [Parsed_amerge_2 @ 0x7fa0faf01bc0] Input channel layouts overlap: output layout will be determined by the number of distinct input channels
  
  Output #0, mp4, to 'mergeab.mp4':
  
    Metadata:
  
      major_brand     : qt  
  
      minor_version   : 0
  
      compatible_brands: qt  
  
      encoder         : Lavf58.29.100
  
      Stream #0:0(und): Video: h264 (libx264) (avc1 / 0x31637661), yuvj420p(pc, progressive), 1280x640, q=-1--1, 29.97 fps, 30k tbn, 29.97 tbc (default)
  
      Metadata:
  
        creation_time   : 2020-02-21T00:35:25.000000Z
  
        handler_name    : Core Media Video
  
        encoder         : Lavc58.54.100 libx264
  
      Side data:
  
        cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
  
      Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s (default)
  
      Metadata:
  
        encoder         : Lavc58.54.100 aac
  
  frame=  318 fps=107 q=-1.0 Lsize=    1568kB time=00:00:17.72 bitrate= 724.8kbits/s speed=5.99x    
  
  video:1282kB audio:272kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.902630%
  
  [libx264 @ 0x7fa0fb004000] frame I:2     Avg QP:19.72  size: 51154
  
  [libx264 @ 0x7fa0fb004000] frame P:148   Avg QP:21.28  size:  6644
  
  [libx264 @ 0x7fa0fb004000] frame B:168   Avg QP:25.18  size:  1351
  
  [libx264 @ 0x7fa0fb004000] consecutive B-frames:  1.3% 84.9%  0.0% 13.8%
  
  [libx264 @ 0x7fa0fb004000] mb I  I16..4: 23.2% 49.3% 27.5%
  
  [libx264 @ 0x7fa0fb004000] mb P  I16..4:  2.1%  2.0%  0.2%  P16..4: 29.0%  5.1%  4.8%  0.0%  0.0%    skip:56.8%
  
  [libx264 @ 0x7fa0fb004000] mb B  I16..4:  0.5%  0.3%  0.0%  B16..8: 16.7%  0.8%  0.2%  direct: 6.1%  skip:75.4%  L0:24.8% L1:71.1% BI: 4.1%
  
  [libx264 @ 0x7fa0fb004000] 8x8 transform intra:45.4% inter:74.3%
  
  [libx264 @ 0x7fa0fb004000] coded y,uvDC,uvAC intra: 29.3% 51.4% 11.6% inter: 5.7% 16.6% 0.3%
  
  [libx264 @ 0x7fa0fb004000] i16 v,h,dc,p: 11% 53% 10% 26%
  
  [libx264 @ 0x7fa0fb004000] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 13% 36% 35%  2%  2%  2%  4%  2%  3%
  
  [libx264 @ 0x7fa0fb004000] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 24% 30% 12%  4%  6%  5%  7%  5%  6%
  
  [libx264 @ 0x7fa0fb004000] i8c dc,h,v,p: 47% 33% 17%  4%
  
  [libx264 @ 0x7fa0fb004000] Weighted P-Frames: Y:0.0% UV:0.0%
  
  [libx264 @ 0x7fa0fb004000] ref P L0: 76.6%  7.6% 11.1%  4.7%
  
  [libx264 @ 0x7fa0fb004000] ref B L0: 89.1% 10.3%  0.6%
  
  [libx264 @ 0x7fa0fb004000] ref B L1: 99.7%  0.3%
  
  [libx264 @ 0x7fa0fb004000] kb/s:989.60
  
  [aac @ 0x7fa0fb005800] Qavg: 13376.354