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• Resizing AVIF images with transparency with FFmpeg [closed]

  4 octobre 2024, par Calebmer

  I'm trying to resize an image with transparency with FFmpeg, however the output looks to only be a resized version of the alpha layer.

  


  When I try to do a noop transform of the AVIF image with an alpha layer :

  


  ffmpeg -i input.avif output.avif


  


  output.avif appears to be the alpha layer with black representing alpha 0 and white representing alpha 1.

  


  ffprobe input.avif gives me :

  


  ffprobe version 7.0.2 Copyright (c) 2007-2024 the FFmpeg developers
  built with Apple clang version 15.0.0 (clang-1500.3.9.4)
  configuration: --prefix=/opt/homebrew/Cellar/ffmpeg/7.0.2 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags='-Wl,-ld_classic' --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libaribb24 --enable-libbluray --enable-libdav1d --enable-libharfbuzz --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --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-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox --enable-audiotoolbox --enable-neon
  libavutil      59.  8.100 / 59.  8.100
  libavcodec     61.  3.100 / 61.  3.100
  libavformat    61.  1.100 / 61.  1.100
  libavdevice    61.  1.100 / 61.  1.100
  libavfilter    10.  1.100 / 10.  1.100
  libswscale      8.  1.100 /  8.  1.100
  libswresample   5.  1.100 /  5.  1.100
  libpostproc    58.  1.100 / 58.  1.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'input.avif':
  Metadata:
    major_brand     : avif
    minor_version   : 0
    compatible_brands: avifmif1miaf
  Duration: N/A, start: 0.000000, bitrate: N/A
  Stream #0:0[0x1]: Video: av1 (libdav1d) (Main) (av01 / 0x31307661), gray(pc), 336x252 [SAR 1:1 DAR 4:3], 1 fps, 1 tbr, 1 tbn (default)
  Stream #0:1[0x2]: Video: av1 (libdav1d) (High) (av01 / 0x31307661), yuv444p(pc, smpte170m/bt709/iec61966-2-1), 336x252 [SAR 1:1 DAR 4:3], 1 fps, 1 tbr, 1 tbn


  


  Seeing there are two streams (the first stream being gray(pc), probably the alpha layer) I next tried :

  


  ffmpeg -i input.avif -map 0:v:1 output.avif


  


  To see the second stream and it gave me the image without any alpha channel. Transparent pixels were black.

  


  Ultimately I want to resize the AVIF file with ffmpeg -i input.avif -vf "scale=iw/2:-1" output.avif but that appears to only resize the greyscale alpha channel. Furthermore, this will be part of a script that operates on some AVIF files without an alpha channel and some AVIF files with an alpha channel and I don't know which files have an alpha channel ahead of time. ffmpeg -i input.avif -vf "scale=iw/2:-1" output.avif works for files without an alpha channel.

  


• Marketing Cohort Analysis : How To Do It (With Examples)

  12 janvier 2024, par Erin

  The better you understand your customers, the more effective your marketing will become. 

  The good news is you don’t need to run expensive focus groups to learn much about how your customers behave. Instead, you can run a marketing cohort analysis using data from your website analytics.

  A marketing cohort groups your users by certain traits and allows you to drill down to discover why they take the actions on your website they do. 

  In this article, we’ll explain what a marketing cohort analysis is, show you what you can achieve with this analytical technique and provide a step-by-step guide to pulling it off. 

  What is cohort analysis in marketing ?

  A marketing cohort analysis is a form of behavioural analytics where you analyse the behavioural patterns of users who share a similar trait to better understand their actions. 

  These shared traits could be anything like the date they signed up for your product, users who bought your service through a paid ad or email subscribers from the United Kingdom.

  It’s a fantastic way to improve your marketing efforts, allowing you to better understand complex user behaviours, personalise campaigns accordingly and improve your ROI. 

  You can run marketing analysis using an analytics platform like Google Analytics or Matomo. With these platforms, you can measure how cohorts perform using traffic, engagement and conversion metrics.

  

  There are two types of cohort analysis : acquisition-based cohort analysis and behavioural-based cohort analysis.

  Acquisition-based cohort analysis

  An acquisition-based cohort divides users by the date they purchased your product or service and tracks their behaviour afterward. 

  For example, one cohort could be all the users who signed up for your product in November. Another could be the users who signed up for your product in October. 

  You could then run a cohort analysis to see how the behaviour of the two cohorts differed. 

  Did the November cohort show higher engagement rates, increased frequency of visits post-acquisition or quicker conversions compared to the October cohort ? Analysing these cohorts can help with refining marketing strategies, optimising user experiences and improving retention and conversion rates.

  As you can see from the example, acquisition-based cohorts are a great way to track the initial acquisition and how user behaviour evolves post-acquisition.

  Behavioural-based cohort analysis

  A behavioural-based cohort divides users by their actions on your site. That could be their bounce rate, the number of actions they took on your site, their average time on site and more.

  

  Behavioural cohort analysis gives you a much deeper understanding of user behaviour and how they interact with your website.

  What can you achieve with a marketing cohort analysis ?

  A marketing cohort analysis is a valuable tool that can help marketers and product teams achieve the following goals :

  

  Understand which customers churn and why

  Acquisition and behavioural cohort analyses help marketing teams understand when and why customers leave. This is one of the most common goals of a marketing cohort analysis. 

  Learn which customers are most valuable

  Want to find out which channels create the most valuable customers or what actions customers take that increase their loyalty ? You can use a cohort analysis to do just that. 

  For example, you may find out you retain users who signed up via direct traffic better than those that signed up from an ad campaign. 

  Discover how to improve your product

  You can even use cohort analysis to identify opportunities to improve your website and track the impact of your changes. For example, you could see how visitor behaviour changes after a website refresh or whether visitors who take a certain action make more purchases. 

  Find out how to improve your marketing campaign

  A marketing cohort analysis makes it easy to find out which campaigns generate the best and most profitable customers. For example, you can run a cohort analysis to determine which channel (PPC ads, organic search, social media, etc.) generates customers with the lowest churn rate. 

  If a certain ad campaign generates the low-churn customers, you can allocate a budget accordingly. Alternatively, if customers from another ad campaign churn quickly, you can look into why that may be the case and optimise your campaigns to improve them. 

  Measure the impact of changes

  You can use a behavioural cohort analysis to understand what impact changes to your website or product have on active users. 

  If you introduced a pricing page to your website, for instance, you could analyse the behaviour of visitors who interacted with that page compared to those who didn’t, using behavioural cohort analysis to gauge the impact of these website changes on engagemen or conversions.

  The problem with cohort analysis in Google Analytics

  Google Analytics is often the first platform marketers turn to when they want to run a cohort analysis. While it’s a free solution, it’s not the most accurate or easy to use and users often encounter various issues. 

  For starters, Google Analytics can’t process user visitor data if they reject cookies. This can lead to an inaccurate view of traffic and compromise the reliability of your insights.

  In addition, GA is also known for sampling data, meaning it provides a subset rather than the complete dataset. Without the complete view of your website’s performance, you might make the wrong decisions, leading to less effective campaigns, missed opportunities and difficulties in reaching marketing goals.

  How to analyse cohorts with Matomo

  Luckily, there is an alternative to Google Analytics. 

  As the leading open-source web analytics solution, Matomo offers a robust option for cohort analysis. With its 100% accurate data, thanks to the absence of sampling, and its privacy-friendly tracking, users can rely on the data without resorting to guesswork. It is a premium feature included with our Matomo Cloud or available to purchase on the Matomo Marketplace for Matomo On-Premise users.

  Below, we’ll show how you can run a marketing cohort analysis using Matomo.

  Set a goal

  Setting a goal is the first step in running a cohort analysis with any platform. Define what you want to achieve from your analysis and choose the metrics you want to measure. 

  For example, you may want to improve your customer retention rate over the first 90 days. 

  Define cohorts

  Next, create cohorts by defining segmentation criteria. As we’ve discussed above, this could be acquisition-based or behavioural. 

  Matomo makes it easy to define cohorts and create charts. 

  In the sidebar menu, click Visitors > Cohorts. You’ll immediately see Matomo’s standard cohort report (something like the one below).

  

  In the example above, we’ve created cohorts by bounce rate. 

  You can view cohorts by weekly, monthly or yearly periods using the date selector and change the metric using the dropdown. Other metrics you can analyse cohorts by include :

  • Unique visitors
  • Return visitors
  • Conversion rates
  • Revenue
  • Actions per visit

  Change the data selection to create your desired cohort, and Matomo will automatically generate the report. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Analyse your cohort chart

  Cohort charts can be intimidating initially, but they are pretty easy to understand and packed with insights. 

  Here’s an example of an acquisition-based cohort chart from Matomo looking at the percentage of returning visitors :

  

  Cohorts run vertically. The oldest cohort (visitors between February 13 – 19) is at the top of the chart, with the newest cohort (April 17 – 23) at the bottom. 

  The period of time runs horizontally — daily in this case. The cells show the corresponding value for the metric we’re plotting (the percentage of returning visitors). 

  For example, 98.69% of visitors who landed on your site between February 13 – 19, returned two weeks later. 

  Usually, running one cohort analysis isn’t enough to identify a problem or find a solution. That’s why comparing several cohort analyses or digging deeper using segmentation is important.

  Segment your cohort chart

  Matomo lets you dig deeper by segmenting each cohort to examine their behaviour’s specifics. You can do this from the cohort report by clicking the segmented visitor log icon in the relevant row.

  

  

  Segmenting cohorts lets you understand why users behave the way they do. For example, suppose you find that users you purchased on Black Friday don’t return to your site often. In that case, you may want to rethink your offers for next year to target an audience with potentially better customer lifetime value. 

  Start using Matomo for marketing cohort analysis

  A marketing cohort analysis can teach you a lot about your customers and the health of your business. But you need the right tools to succeed. 

  Matomo provides an effective and privacy-first way to run your analysis. You can create custom customer segments based on almost anything, from demographics and geography to referral sources and user behaviour. 

  Our custom cohort analysis reports and colour-coded visualisations make it easy to analyse cohorts and spot patterns. Best of all, the data is 100% accurate. Unlike other web analytics solution or cohort analysis tools, we don’t sample data. 

  Find out how you can use Matomo to run marketing cohort analysis by trialling us free for 21 days. No credit card required.

  Try Matomo for Free

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• How to reinsert edited metadata stream information from the FFMETADATAFILE file ? [closed]

  6 septembre 2024, par SENYCH

  I'm working on simplifying and speeding up the process of editing video metadata for user convenience. I've successfully edited metadata streams using console commands, such as :

  


  ffmpeg -i INPUT.mp4 -map 0 -metadata:s:0 "handler_name=An other video" -metadata:s:1 "handler_name=An other audio recording in russian" -metadata:s:2 "handler_name=An other audio recording in english" -metadata:s:3 "handler_name=An other audio recording in japanese" -c copy OUTPUT.mp4


  


  However, I'd like to accomplish this through a ffmetadata file. Here's the approach I've taken :

  


  ffmpeg -t 0 -i INPUT.mp4 -map 0 -c copy -f ffmetadata ffmetadata.txt -hide_banner


  


  Original ffmetadata.txt is :

  


  ;FFMETADATA1
major_brand=isom
minor_version=512
compatible_brands=isomiso2avc1mp41
encoder=Lavf61.5.101
[STREAM]
language=und
handler_name=The best video
vendor_id=[0][0][0][0]
[STREAM]
language=rus
handler_name=The best russian language
vendor_id=[0][0][0][0]
[STREAM]
language=eng
handler_name=The best english language
vendor_id=[0][0][0][0]
[STREAM]
language=jpn
handler_name=The best japanese language
vendor_id=[0][0][0][0]


  


  Edit the ffmetadata.txt file to update the handler_name values :

  


  ;FFMETADATA1
major_brand=isom
minor_version=512
compatible_brands=isomiso2avc1mp41
encoder=Lavf61.5.101
[STREAM]
language=und
handler_name=An other video
vendor_id=[0][0][0][0]
[STREAM]
language=rus
handler_name=An other audio recording in russian
vendor_id=[0][0][0][0]
[STREAM]
language=eng
handler_name=An other audio recording in english
vendor_id=[0][0][0][0]
[STREAM]
language=jpn
handler_name=An other audio recording in japanese
vendor_id=[0][0][0][0]


  


  Attempt to apply the updated metadata from ffmetadata2.txt :

  


  C:\Users\Alexander\Videos>ffmpeg -i INPUT.mp4 -i ffmetadata2.txt -map 0:v -map 0:a -map_metadata 1 -c copy OUTPUT2.mp4 -hide_banner


  


  Despite these steps, I've noticed that only the global metadata is updated, while the metadata for each stream remains unchanged. The console output shows that metadata for each stream is not updated as expected.

  


  What am I missing ? How can I ensure that the stream-specific metadata is also updated correctly when using a ffmetadata file ?

  


  Additional Information :

  


  

  • FFmpeg version : 2024-08-26-git-98610fe95f-full_build

  


  • The ffmetadata file format and the approach I've used should be correct according to the FFmpeg documentation.

  


  


  I would greatly appreciate any recommendations or suggestions on how to solve this problem !

  


  I found a bad solution for my problem, but it still isn't ideal as it requires specifying -map_metadata:s:N 1:s:N for each stream individually, which is quite cumbersome. Is there a way to simplify this process and avoid having to set metadata for each stream separately ?

  


  The command I’m using is :

  


  C:\Users\Alexander\Videos>ffmpeg -i INPUT.mp4 -i ffmetadata2.txt -map 0 -map_metadata:s:0 1:s:0 -map_metadata:s:1 1:s:1 -map_metadata:s:2 1:s:2 -map_metadata:s:3 1:s:3 -c copy OUTPUT2.mp4 -hide_banner


  


  This works, but having to specify -map_metadata:s:N for each stream creates extra work, especially as the number of streams increases. Is there a more efficient way to handle this ?