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• ffmpeg - spark - azure databricks - error writing trailer of "filename.mp3" : Operation not supported

  4 juillet 2021, par CRAFTY DBA

  I have been trying to figure out this tough problem.

  


  I am trying to convert *.mp4 files to *.mp3 files.

  


  I tried using MoviePy but I found out that is uses ffmpeg and was having the same issue.

  


  I used these two articles to get the latest version of ffmpeg installed on the Azure Databricks Cluster during startup. I am using a single node cluster for this POC code.

  


  Pyspark : Use ffmpeg on the driver and workers
  
https://ubuntuhandbook.org/index.php/2020/06/install-ffmpeg-4-3-via-ppa-ubuntu-18-04-16-04
  

  


  The issue is that even the simplest command results in errors.

  


  %%bash ffmpeg -i /dbfs/Craftydba/recording.mp4 /dbfs/Craftydba/recording.mp3

  


  I even tried .wav as an output format and still the same issue.

  


  I retested this command on a Data Science VM in Azure with Python and FFMPEG. It works fine on that OS/Build.

  


  It has something to do with the version of the code on the spark cluster.

  


  Any help is appreciated.

  


  Sincerely

  


  John Miner

  


  PS : I am add a dump of the OS version as well as a ffmpeg error.

  


  Os Version Dump

  


  NAME="Ubuntu"
VERSION="18.04.5 LTS (Bionic Beaver)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 18.04.5 LTS"
VERSION_ID="18.04"
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
VERSION_CODENAME=bionic
UBUNTU_CODENAME=bionic

  


  FFMPEG Dump

  


  ffmpeg version 4.3.2-0york0~18.04 Copyright (c) 2000-2021 the FFmpeg developers
  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)
  configuration: --prefix=/usr --extra-version='0york0~18.04' --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libzimg --enable-pocketsphinx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  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
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/dbfs/Craftydba/recording.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2
    creation_time   : 2021-06-18T19:07:17.000000Z
  Duration: 00:04:48.64, start: 0.000000, bitrate: 1065 kb/s
    Stream #0:0(eng): Video: h264 (Constrained Baseline) (avc1 / 0x31637661), yuv420p, 1920x1080, 1000 kb/s, 7.96 fps, 8 tbr, 10k tbn, 20k tbc (default)
    Metadata:
      creation_time   : 2021-06-18T19:07:17.000000Z
    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 16000 Hz, mono, fltp, 63 kb/s (default)
    Metadata:
      creation_time   : 2021-06-18T19:07:17.000000Z
Stream mapping:
  Stream #0:1 -> #0:0 (aac (native) -> mp3 (libmp3lame))
Press [q] to stop, [?] for help
Output #0, mp3, to '/dbfs/Craftydba/recording.mp3':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2
    TSSE            : Lavf58.45.100
    Stream #0:0(eng): Audio: mp3 (libmp3lame), 16000 Hz, mono, fltp (default)
    Metadata:
      creation_time   : 2021-06-18T19:07:17.000000Z
      encoder         : Lavc58.91.100 libmp3lame
Error writing trailer of /dbfs/Craftydba/recording.mp3: Operation not supported
size=     846kB time=00:04:48.65 bitrate=  24.0kbits/s speed=86.9x    
video:0kB audio:846kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.036945%


  


• 10 Proven Ways Heatmaps Improve Website Conversions

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

  Heatmap analytics are critical in improving website conversions. Why ? Because they provide customer-centric insights. 

  In the online market, businesses that are customer-centric are 60% more profitable than businesses that are not.

  Using heatmaps to track factors such as usability, compare A/B landing pages and content engagement across channels optimises online conversions by addressing issues faced by real users. 

  How heatmaps benefit your customers

  Customer experience is one of the most important factors in business success. 

  Website heatmap software like Matomo offers unique insights into customer behaviour that is then used to improve their experience, usability and engagement. 

  Data analysis captures information on how many people complete a sales funnel or bounce from a website. Behavioural analytics like heatmaps can show you why they bounce.

  This benefits your customers (and therefore your bottom line) because it puts the focus on them and their needs.

  10 ways heatmap analytics help increase website conversions

  #1. Improve UX/Usability 

  Heatmap analytics improve usability by identifying where you are losing customers on your website.

  Forrester research indicates that improving user experience can improve conversions by up to 400%, and on average every $1 spent on UX has a return of $100. 

  For example, you may have a CTA button but customers never click it to reach the payment page. 

  Heatmaps show you how customers interact with your website naturally so that you can adjust it according to their needs.

  Using heatmap analytics to improve usability boosts conversions because it improves customer experiences. 88% of online consumers say that they wouldn’t even bother returning to a website after a bad experience. 

  #2. Website design and content structure 

  Another way that heatmaps can improve conversions is to analyse your website design and content structure. 

  You might be wondering how often a specific ad or a banner was displayed and viewed by your visitors on any of your pages and how often a visitor actually interacted with them. These two parts of the analysis are called content impression and content interaction.

  Ideally, your website elements such as banners, listings, buttons and thumbnails will entice customers to click and find out more. 

  Heatmaps and click maps analyse

  1. How many impressions the content has (e.g. a banner), and
  2. What percent of users that see the content click on it 

  For example, you may have a banner with high impressions but low click-through rates. Tracking content interactions optimises your website by showing which elements or CTAs need more visibility. 

  #3. A/B testing

  Heatmaps provide invaluable data on which landing pages are converting the best. Not only that, but session recordings and heatmap data can show you exactly why one is converting better so that you can replicate the results to increase conversions on other landing pages.

  Tracking heatmap updates on different versions of the same sales page will help confirm creative solutions faster than feedback alone. 

  Ultimately this kind of comparison increases your ROI faster because you are not guessing why some customers are converting and others are not. 

  #4. Conversion Funnel

  Using heatmap software in sales funnels lets you visualise user behaviour at each stage of the conversion process. 

  For example, if many customers are dropping off a payment page, heatmaps can indicate whether it is a usability issue such as pop ups, lack of clarity with payment buttons or something web developers haven’t seen from the back end. 

  These analytics improve conversions by reducing friction in sales funnels as much as possible. 

  #5. Content engagement across channels 

  Optimising websites across all channels is now expected for online businesses. 

  Bad mobile optimisation annoys 48% of online shoppers, and if your web page takes longer than 3 seconds to load, 53% of visitors will simply click away. 

  You can use heatmaps to improve engagement by tracking mouse activity, clicks and scrolling. This helps improve conversions by confirming 

  • How invested a user is in the page 
  • How easy it is to navigate your website and content on different devices 
  • What is your most viewed content and what to push more of 
  • How users generally move through your website on different devices 
  • How clear your messaging is (e.g. high click through rate but low engagement could indicate they aren’t finding what they’re looking for once they click on a CTA)

  #6. Above the fold analysis 

  Although a well-used web development term, above the fold is still one of the most important factors in heatmap analysis. 

  Above the fold analysis gives you insight into a customer’s first impression of a page. 

  An example of above-the-fold heatmaps in action could be a page with a video explanation. Say you have a landing page with a video below the fold that explains why someone should buy and has a CTA button underneath. If there are a lot of page visitors but very few people scrolling below the fold, you can see why hardly any visitors are watching the video or engaging with the CTA button. 

  Insights like this would inform further development such as including important video content above the fold or updating header copy to encourage visitors to scroll down the page more often.

  

  #7. Session recording

  Recording features go hand in hand with heatmap visualisations. Recording features like Session Recording shows the flow of each user’s time on your website. 

  For example, a session recording replays all clicks, mouse movements, scrolls, window resizes, form interactions, and page changes (e.g. when a popup appears).

  #8. Scroll heatmap 

  A scroll heatmap shows the percentage of people that have seen a part of the page. 

  For example, the top of a website page will be the “hottest” in a scroll heatmap, and it naturally gets “colder” further down.

  Tracking this shows whether customers are staying on the page, whether they are only seeing information above the fold, and whether sales pages are engaging. 

  It is an effective strategy for improving sales pages because it shows where customers are losing interest and which elements receive the most engagement.

  

  #9. Records clicks 

  With a click heatmap, you can find out what your visitors think is clickable on a webpage.

  This improves conversions in two ways. 

  Firstly, it shows whether customers are clicking where you expect them to. For example, if you create a “buy now” or “free trial” button but nobody ever pushes it, it informs your back end developers that it needs an upgrade. 

  Secondly, it indicates any user experience issues. If there are a lot of clicks on an element that doesn’t link anywhere, it shows that it either needs to be changed or have a link included because customers are trying to engage with it. 

  For even more accurate data, combine click maps with hover maps. This shows where users are paying attention but not clicking through. 

  #10. Records mouse movement/hovering

  Is your website distracting users from the ultimate goal of converting ? Does your website have a logical flow and next step ? Recording mouse movement and attention will help you answer questions like these. 

  Mouse move and hover heatmaps identify where your website visitors engage on the page. Are they naturally drawn to your CTAs ? Is the sidebar taking their attention away from the primary content ? 

  This data increases the likelihood of conversions because it shows where you need to remove distractions or draw their attention in. 

  

  Final thoughts on heatmap analytics 

  Heatmap analytics benefit both you and your customers. By identifying issues that stop them from buying and optimise their engagement, you’ll have happy customers and happy stakeholders. 

  Next, check out these guides on heatmap software and using user behaviour analytics to increase conversions and improve customer experience !

  The Ultimate Guide to Heatmap Software

  Heatmap Video

  Session Recording Video

• Recording Camlink 4k feed with ffmpeg

  5 août 2020, par Nitzan Yogev

  its my first question ! awesome !
so im new to python, and im working on some program for work
I have the Elgato CamLink 4k connected to Sony A7III.
im looking for the best way to record a video file from the camlink feed using python
I know that ffmpeg is a good way to record webcam feed. but im having trouble with it
here is my code.

  


      import ffmpeg

(
    ffmpeg
    .input('0', format='avfoundation', pix_fmt='nv12', framerate=25)
    .output('test.mp4', pix_fmt='nv12', vframes=125)
    .overwrite_output()
    .run()
)


  


  im using this ffmpeg-python module

  


  im getting a output file with only the first frame and nothing more

  


  here is what im getting in the run terminal

  


  ffmpeg version 4.3.1 Copyright (c) 2000-2020 the FFmpeg developers
  built with Apple clang version 11.0.3 (clang-1103.0.32.62)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/4.3.1 --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
[avfoundation @ 0x7ff043814600] Stream #0: not enough frames to estimate rate; consider increasing probesize
Input #0, avfoundation, from '0':
  Duration: N/A, start: 6247.240967, bitrate: N/A
    Stream #0:0: Video: rawvideo (NV12 / 0x3231564E), nv12, 3840x2160, 1000k tbr, 1000k tbn, 1000k tbc
Stream mapping:
  Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[mp4 @ 0x7ff043856c00] Frame rate very high for a muxer not efficiently supporting it.
Please consider specifying a lower framerate, a different muxer or -vsync 2
[libx264 @ 0x7ff043868000] MB rate (32400000000) > level limit (16711680)
[libx264 @ 0x7ff043868000] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x7ff043868000] profile High, level 6.2, 4:2:0, 8-bit
[libx264 @ 0x7ff043868000] 264 - core 160 r3011 cde9a93 - H.264/MPEG-4 AVC codec - Copyleft 2003-2020 - 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=18 lookahead_threads=3 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
Output #0, mp4, to 'test.mp4':
  Metadata:
    encoder         : Lavf58.45.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), nv12, 3840x2160, q=-1--1, 1000k fps, 1000k tbn, 1000k tbc
    Metadata:
      encoder         : Lavc58.91.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
frame=  125 fps= 30 q=-1.0 Lsize=     788kB time=00:00:00.00 bitrate=52509528.5kbits/s dup=124 drop=1 speed=2.99e-05x    
video:786kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.290685%
[libx264 @ 0x7ff043868000] frame I:1     Avg QP:19.49  size:588332
[libx264 @ 0x7ff043868000] frame P:31    Avg QP:20.24  size:  4202
[libx264 @ 0x7ff043868000] frame B:93    Avg QP:33.28  size:   922
[libx264 @ 0x7ff043868000] consecutive B-frames:  0.8%  0.0%  0.0% 99.2%
[libx264 @ 0x7ff043868000] mb I  I16..4:  2.8% 86.0% 11.3%
[libx264 @ 0x7ff043868000] mb P  I16..4:  0.0%  0.0%  0.0%  P16..4:  6.9%  0.3%  0.5%  0.0%  0.0%    skip:92.3%
[libx264 @ 0x7ff043868000] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  4.4%  0.0%  0.0%  direct: 0.0%  skip:95.6%  L0:19.0% L1:81.0% BI: 0.0%
[libx264 @ 0x7ff043868000] 8x8 transform intra:86.0% inter:88.3%
[libx264 @ 0x7ff043868000] coded y,uvDC,uvAC intra: 93.8% 87.1% 55.6% inter: 0.1% 1.5% 0.0%
[libx264 @ 0x7ff043868000] i16 v,h,dc,p:  9% 10%  9% 72%
[libx264 @ 0x7ff043868000] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 15% 14% 34%  6%  5%  5%  6%  7%  7%
[libx264 @ 0x7ff043868000] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 18% 16%  9%  8%  7%  8%  7%  6%
[libx264 @ 0x7ff043868000] i8c dc,h,v,p: 48% 26% 20%  6%
[libx264 @ 0x7ff043868000] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x7ff043868000] ref P L0: 82.6%  0.1% 14.0%  3.3%
[libx264 @ 0x7ff043868000] ref B L0: 99.8%  0.2%  0.0%
[libx264 @ 0x7ff043868000] ref B L1: 76.2% 23.8%
[libx264 @ 0x7ff043868000] kb/s:51475456.00


  


  if im plugging the camlink off I get a video from my macbook internal camera. so I know this should somehow work