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• Moviepy not updating FFmpeg Version after FFmpeg Install ?

  31 mai 2024, par The_ Game12

  I've been toying around with MoviePy, and recently switched a project to a new computer at home to continue messing around with it. However, I tried running what I had previously written (which ran perfectly fine on the other computer) and I get this :

  


  OSError: MoviePy error: failed to read the first frame of video file ./Gameplay/minecraft-
gameplay2.mp4. That might mean that the file is corrupted. That may also mean that you are using 
a deprecated version of FFMPEG. On Ubuntu/Debian for instance the version in the repos is 
deprecated. Please update to a recent version from the website.



  


  After reading the error, I did as it instructed, and updated my FFmpeg :

  


  $ ffmpeg
ffmpeg version N-115387-g8e27bd025f-20240525 Copyright (c) 2000-2024 the FFmpeg developers
  built with gcc 13.2.0 (crosstool-NG 1.26.0.65_ecc5e41)
  configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-ffbuild-linux-gnu- --arch=x86_64 --target-os=linux --enable-gpl --enable-version3 --disable-debug --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-openssl --enable-fontconfig --enable-libharfbuzz --enable-libvorbis --enable-opencl --enable-libpulse --enable-libvmaf --enable-libxcb --enable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --enable-libdvdread --enable-libdvdnav --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libaribcaption --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --disable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --enable-libdrm --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-libs='-ldl -lgomp' --extra-ldflags=-pthread --extra-ldexeflags=-pie --cc=x86_64-ffbuild-linux-gnu-gcc --cxx=x86_64-ffbuild-linux-gnu-g++ --ar=x86_64-ffbuild-linux-gnu-gcc-ar --ranlib=x86_64-ffbuild-linux-gnu-gcc-ranlib --nm=x86_64-ffbuild-linux-gnu-gcc-nm --extra-version=20240525
  libavutil      59. 20.100 / 59. 20.100
  libavcodec     61.  5.104 / 61.  5.104
  libavformat    61.  3.104 / 61.  3.104
  libavdevice    61.  2.100 / 61.  2.100
  libavfilter    10.  2.102 / 10.  2.102
  libswscale      8.  2.100 /  8.  2.100
  libswresample   5.  2.100 /  5.  2.100
  libpostproc    58.  2.100 / 58.  2.100
Universal media converter
usage: ffmpeg [options] [[infile options] -i infile]... {[outfile options] outfile}...


  


  And I continued getting the same error. So I looked at what version my moviepy was using, and it was still 4.X. I read somewhere that your FFmpeg version is determined on the first use, which made sense, so I uninstalled and reinstalled, to get the same error.

  


  I am honestly lost at this point, as I have the newest version of FFmpeg, but I still get this from moviepy :

  


  >>> import moviepy&#xA;>>> print(moviepy.config.FFPMEG_BINARY)&#xA;ffmpeg : /home/<username>/.local/lib/python3.9/site-packages/imageio_ffmpeg/binaries/ffmpeg-linux64-v4.2.2&#xA;</username>

  


  Any Ideas as to what I'm doing wrong ?

  


  (Note : I'm using Crostini which I believe is using an Ubuntu or Ubuntu-Like shell)

  


  Thanks :)

  


• "Error opening input" error when running ffmpeg via shell script

  15 octobre 2024, par Roni Yaniv

  I'm on a macbook running sonoma 14.7.

  


  I have the following ffmpeg command which runs just fine by itself in the same folder of the files I need to modify :

  


  ffmpeg -i "water-stream-90fps.mp4" -vf "drawtext=text='Frame\: %{frame_num}': start_number=1: x=(w-text_w): y=(h-text_h):fontsize=(h*10/100):fontcolor=yellow,drawtext=text='%{pts\:hms}':x=0:y=0:fontsize=(h*10/100):fontcolor=yellow" "water-stream-90fps-with-markers.mp4"

  


  However, when I try to use it in a shell script by running ./add_markers.sh in the same folder, I get an error.

  


  This is the add_markers.sh script :

  


  #!/bin/bash

# Replace '/path/to/your/folder' with the actual path to your folder
folder=$(pwd)

# Replace 'ffmpeg -i input.mp4 -vf scale=640:480 output.mp4' with your desired FFmpeg command
ffmpeg_command="ffmpeg -i input.mp4 -vf \"drawtext=text='Frame\: %{frame_num}': start_number=1: x=(w-text_w): y=(h-text_h):fontsize=(h*10/100):fontcolor=yellow,drawtext=text='%{pts\:hms}':x=0:y=0:fontsize=(h*10/100):fontcolor=yellow\" output.mp4"

echo "$ffmpeg_command"

# Loop through all files in the folder and execute the FFmpeg command

for file in "$folder"/*; do
    filename=$(basename "${file%.*}") #get only the first part of the file name without path or extension

    # Replace 'input.mp4' with the current filename and 'output.mp4' with the new filename. 
    # Added double quotes to manage filenames with spaces.
    new_command="${ffmpeg_command//input.mp4/\"$filename.mp4\"}"
    new_command="${new_command//output.mp4/\"$filename-with-markers.mp4\"}"

    echo "Running $new_command"

    $new_command

done


  


  This is the error I get (for one of the files, same error for all of them) :

  


  Running ffmpeg -i "water-stream-90fps.mp4" -vf "drawtext=text='Frame\: %{frame_num}': start_number=1: x=(w-text_w): y=(h-text_h):fontsize=(h*10/100):fontcolor=yellow,drawtext=text='%{pts\:hms}':x=0:y=0:fontsize=(h*10/100):fontcolor=yellow" "water-stream-90fps-with-markers.mp4"
ffmpeg version 7.0 Copyright (c) 2000-2024 the FFmpeg developers
  built with Apple clang version 15.0.0 (clang-1500.3.9.4)
  configuration: --prefix=/opt/homebrew/Cellar/ffmpeg/7.0 --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-libopenvino --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
[in#0 @ 0x6000015bc300] Error opening input: No such file or directory
Error opening input file "water-stream-90fps.mp4".
Error opening input files: No such file or directory


  


  I tried all kinds of things but my brain is starting to swell. Any guidance/help would be appreciated.

  


• A Complete Guide to Metrics in Google Analytics

  11 janvier 2024, par Erin

  There’s no denying that Google Analytics is the most popular web analytics solution today. Many marketers choose it to understand user behaviour. But when it offers so many different types of metrics, it can be overwhelming to choose which ones to focus on. In this article, we’ll dive into how metrics work in Google Analytics 4 and how to decide which metrics may be most useful to you, depending on your analytics needs.

  However, there are alternative web analytics solutions that can provide more accurate data and supplement GA’s existing features. Keep reading to learn how to overcome Google Analytics limitations so you can get the more out of your web analytics.

  What is a metric in Google Analytics ?

  In Google Analytics, a metric is a quantitative measurement or numerical data that provides insights into specific aspects of user behaviour. Metrics represent the counts or sums of user interactions, events or other data points. You can use GA metrics to better understand how people engage with a website or mobile app. 

  Unlike the previous Universal Analytics (the previous version of GA), GA4 is event-centric and has automated and simplified the event tracking process. Compared to Universal Analytics, GA4 is more user-centric and lets you hone in on individual user journeys. Some examples of common key metrics in GA4 are : 

  • Sessions : A group of user interactions on your website that occur within a specific time period. A session concludes when there is no user activity for 30 minutes.
  • Total Users : The cumulative count of individuals who accessed your site within a specified date range.
  • Engagement Rate : The percentage of visits to your website or app that included engagement (e.g., one more pageview, one or more conversion, etc.), determined by dividing engaged sessions by sessions.

  

  Metrics are invaluable when it comes to website and conversion optimisation. Whether you’re on the marketing team, creating content or designing web pages, understanding how your users interact with your digital platforms is essential.

  GA4 metrics vs. dimensions

  GA4 uses metrics to discuss quantitative measurements and dimensions as qualitative descriptors that provide additional context to metrics. To make things crystal clear, here are some examples of how metrics and dimensions are used together : 

  • “Session duration” = metric, “device type” = dimension 
    • In this situation, the dimension can segment the data by device type so you can optimise the user experience for different devices.
  • “Bounce rate” = metric, “traffic source/medium” = dimension 
    • Here, the dimension helps you segment by traffic source to understand how different acquisition channels are performing. 
  • “Conversion rate” = metric, “Landing page” = dimension 
    • When the conversion rate data is segmented by landing page, you can better see the most effective landing pages. 

  You can get into the nitty gritty of granular analysis by combining metrics and dimensions to better understand specific user interactions.

  How do Google Analytics metrics work ?

  Before diving into the most important metrics you should track, let’s review how metrics in GA4 work. 

  

  1. Tracking code implementation

  The process begins with implementing Google Analytics 4 tracking code into the HTML of web pages. This tracking code is JavaScript added to each website page — it collects data related to user interactions, events and other important tidbits.

  2. Data collection

  As users interact with the website or app, the Google Analytics 4 tracking code captures various data points (i.e., page views, clicks, form submissions, custom events, etc.). This raw data is compiled and sent to Google Analytics servers for processing.

  3. Data processing algorithms

  When the data reaches Google Analytics servers, data processing algorithms come into play. These algorithms analyse the incoming raw data to identify the dataset’s trends, relationships and patterns. This part of the process involves cleaning and organising the data.

  4. Segmentation and customisation

  As discussed in the previous section, Google Analytics 4 allows for segmentation and customisation of data with dimensions. To analyse specific data groups, you can define segments based on various dimensions (e.g., traffic source, device type). Custom events and user properties can also be defined to tailor the tracking to the unique needs of your website or app.

  5. Report generation

  Google Analytics 4 can make comprehensive reports and dashboards based on the processed and segmented data. These reports, often in the form of graphs and charts, help identify patterns and trends in the data.

  What are the most important Google Analytics metrics to track ? 

  In this section, we’ll identify and define key metrics for marketing teams to track in Google Analytics 4. 

  

  1. Pageviews are the total number of times a specific page or screen on your website or app is viewed by visitors. Pageviews are calculated each time a web page is loaded or reloaded in a browser. You can use this metric to measure the popularity of certain content on your website and what users are interested in. 
  2. Event tracking monitors user interactions with content on a website or app (i.e., clicks, downloads, video views, etc.). Event tracking provides detailed insights into user engagement so you can better understand how users interact with dynamic content. 
  3. Retention rate can be analysed with a pre-made overview report that Google Analytics 4 provides. This user metric measures the percentage of visitors who return to your website or app after their first visit within a specific time period. Retention rate = (users with subsequent visits / total users in the initial cohort) x 100. Use this information to understand how relevant or effective your content, user experience and marketing efforts are in retaining visitors. You probably have more loyal/returning buyers if you have a high retention rate. 
  4. Average session duration calculates the average time users spend on your website or app per session. Average session duration = total duration of all sessions / # of sessions. A high average session duration indicates how interested and engaged users are with your content. 
  5. Site searches and search queries on your website are automatically tracked by Google Analytics 4. These metrics include search terms, number of searches and user engagement post-search. You can use site search metrics to better understand user intent and refine content based on users’ searches. 
  6. Entrance and exit pages show where users first enter and leave your site. This metric is calculated by the percentage of sessions that start or end on a specific page. Knowing where users are entering and leaving your site can help identify places for content optimisation. 
  7. Device and browser info includes data about which devices and browsers websites or apps visitors use. This is another metric that Google Analytics 4 automatically collects and categorises during user sessions. You can use this data to improve the user experience on relevant devices and browsers. 
  8. Bounce rate is the percentage of single-page sessions where users leave your site or app without interacting further. Bounce rate = (# of single-page sessions / total # of sessions) x 100. Bounce rate is useful for determining how effective your landing pages are — pages with high bounce rates can be tweaked and optimised to enhance user engagement.

  Examples of how Matomo can elevate your web analytics

  Although Google Analytics is a powerful tool for understanding user behaviour, it also has privacy concerns, limitations and a list of issues. Another web analytics solution like Matomo can help fill those gaps so you can get the most out of your analytics.

  

  1. Cross-verify and validate your observations from Google Analytics by comparing data from Matomo’s Heatmaps and Session Recordings for the same pages. This process grants you access to these advanced features that GA4 does not offer.

  

  1. Matomo provides you with greater accuracy thanks to its privacy-friendly design. Unlike GA4, Matomo can be configured to operate without cookies. This means increased accuracy without intrusive cookie consent screens interrupting the user experience. It’s a win for you and for your users. Matomo also doesn’t apply data sampling so you can rest assured that the data you see is 100% accurate.

  3. Unlike GA4, Matomo offers direct access to customer support so you can save time sifting through community forum threads and online documentation. Gain personalised assistance and guidance for your analytics questions, and resolve issues efficiently.

  

  4. Matomo’s Form Analytics and Media Analytics extend your analytics capabilities beyond just pageviews and event tracking.

     Tracking user interactions with forms can tell you which fields users struggle with, common drop-off points, in addition to which parts of the form successfully guide visitors towards submission.

     See first-hand how Concrete CMS 3x their leads using Matomo’s Form Analytics.

     Media Analytics can provide insight into how users interact with image, video, or audio content on your website. You can use this feature to assess the relevance and popularity of specific content by knowing what your audience is engaged by.

  Try Matomo for Free

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

  Start for free

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  Final thoughts

  Although Google Analytics is a powerful tool on its own, Matomo can elevate your web analytics by offering advanced features, data accuracy and a privacy-friendly design. Don’t play a guessing game with your data — Matomo provides 100% accurate data so you don’t have to rely on AI or machine learning to fill in the gaps. Matomo can be configured cookieless which also provides you with more accurate data and a better user experience. 

  Lastly, Matomo is fully compliant with some of the world’s strictest privacy regulations like GPDR. You won’t have to sacrifice compliance for accurate, high quality data. 

  Start your 21-day free trial of Matomo — no credit card required.

  Try Matomo for Free

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