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• Use ffmpeg to extract picture from m4v file

  31 octobre 2017, par Brian

  I used a program called MetaZ on my mac to tag all my video files (m4v). I am now trying to use these m4v files in Kodi which requires .nfo files and separate picture files for movie posters, etc. I want to extract the picture that is already in the m4v file.

  When I use ffprobe -show_streams, I can see that index4 is a png file (codec_name=png). How do I extract it ? I believe ffmpeg can do it, but can’t figure out how.

  Here is the output from ffprobe :

  Brians-Mac-mini:PythonScript brianjhille$ ffprobe -show_streams badwords.m4v
  
  ffprobe version N-88046-g0cb8369-tessus Copyright (c) 2007-2017 the FFmpeg developers
  
    built with Apple LLVM version 8.0.0 (clang-800.0.42.1)
  
    configuration: --cc=/usr/bin/clang --prefix=/opt/ffmpeg --extra-version=tessus --enable-avisynth --enable-fontconfig --enable-gpl --enable-libass --enable-libbluray --enable-libfreetype --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopus --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzmq --enable-libzvbi --enable-version3 --pkg-config-flags=--static --disable-ffplay
  
    libavutil      56.  0.100 / 56.  0.100
  
    libavcodec     58.  0.100 / 58.  0.100
  
    libavformat    58.  0.100 / 58.  0.100
  
    libavdevice    58.  0.100 / 58.  0.100
  
    libavfilter     7.  0.100 /  7.  0.100
  
    libswscale      5.  0.100 /  5.  0.100
  
    libswresample   3.  0.100 /  3.  0.100
  
    libpostproc    55.  0.100 / 55.  0.100
  
  [mov,mp4,m4a,3gp,3g2,mj2 @ 0x7fd67b002a00] stream 0, timescale not set
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'badwords.m4v':
  
    Metadata:
  
      major_brand     : mp42
  
      minor_version   : 0
  
      compatible_brands: mp42isomavc1
  
      creation_time   : 2014-10-20T13:01:06.000000Z
  
      iTunEXTC        : mpaa|R|400|
  
      title           : Bad Words
  
      artist          : Jason Bateman, Kathryn Hahn, Allison Janney, Philip Baker Hall, Rohan Chand, Ben Falcone, Patricia Belcher, Beth Grant, Rachel Harris, Steve Witting, Greg Cromer
  
      date            : 2013-09-06T11:00:00Z
  
      track           : 0
  
      disc            : 0
  
      season_number   : 0
  
      episode_sort    : 0
  
      description     : A spelling bee loser sets out to exact revenge by finding a loophole and attempting to win as an adult.
  
      synopsis        : A spelling bee loser sets out to exact revenge by finding a loophole and attempting to win as an adult.
  
      encoder         : HandBrake 0.9.9 2013052900
  
      hd_video        : 0
  
      media_type      : 9
  
      genre           : Comedy
  
      iTunMOVI        : <?xml version="1.0" encoding="UTF-8"?>
  
                      : 
  
                      : <plist version="1.0">
  
                      : <dict>
  
                      :   <key>cast</key>
  
                      :   <array>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Jason Bateman</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Kathryn Hahn</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Allison Janney</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Philip Baker Hall</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Rohan Chand</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Ben Falcone</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Patricia Belcher</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Beth Grant</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Rachel Harris</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Steve Witting</string>
  
                      :       </dict>
  
                      :       <dict>
  
                      :           <key>name</key>
  
                      :           <string>Greg Cromer</string>
  
                      :       </dict>
  
                      :   </array>
  
                      : </dict>
  
                      : </plist>
  
                      : 
  
    Duration: 01:29:02.84, start: 0.000000, bitrate: 1339 kb/s
  
      Chapter #0:0: start 0.000000, end 348.214000
  
      Metadata:
  
        title           : Chapter 1
  
      Chapter #0:1: start 348.214000, end 676.542000
  
      Metadata:
  
        title           : Chapter 2
  
      Chapter #0:2: start 676.542000, end 860.058000
  
      Metadata:
  
        title           : Chapter 3
  
      Chapter #0:3: start 860.058000, end 1171.836000
  
      Metadata:
  
        title           : Chapter 4
  
      Chapter #0:4: start 1171.836000, end 1441.839000
  
      Metadata:
  
        title           : Chapter 5
  
      Chapter #0:5: start 1441.839000, end 1632.129000
  
      Metadata:
  
        title           : Chapter 6
  
      Chapter #0:6: start 1632.129000, end 1925.422000
  
      Metadata:
  
        title           : Chapter 7
  
      Chapter #0:7: start 1925.422000, end 2167.030000
  
      Metadata:
  
        title           : Chapter 8
  
      Chapter #0:8: start 2167.030000, end 2409.605000
  
      Metadata:
  
        title           : Chapter 9
  
      Chapter #0:9: start 2409.605000, end 2748.276000
  
      Metadata:
  
        title           : Chapter 10
  
      Chapter #0:10: start 2748.276000, end 2917.945000
  
      Metadata:
  
        title           : Chapter 11
  
      Chapter #0:11: start 2917.945000, end 3309.502000
  
      Metadata:
  
        title           : Chapter 12
  
      Chapter #0:12: start 3309.502000, end 3634.660000
  
      Metadata:
  
        title           : Chapter 13
  
      Chapter #0:13: start 3634.660000, end 3942.434000
  
      Metadata:
  
        title           : Chapter 14
  
      Chapter #0:14: start 3942.434000, end 4101.626000
  
      Metadata:
  
        title           : Chapter 15
  
      Chapter #0:15: start 4101.626000, end 4336.193000
  
      Metadata:
  
        title           : Chapter 16
  
      Chapter #0:16: start 4336.193000, end 4620.643000
  
      Metadata:
  
        title           : Chapter 17
  
      Chapter #0:17: start 4620.643000, end 4873.729000
  
      Metadata:
  
        title           : Chapter 18
  
      Chapter #0:18: start 4873.729000, end 5153.341000
  
      Metadata:
  
        title           : Chapter 19
  
      Chapter #0:19: start 5153.341000, end 5342.796000
  
      Metadata:
  
        title           : Chapter 20
  
      Stream #0:0(und): Video: h264 (Constrained Baseline) (avc1 / 0x31637661), yuv420p(tv, smpte170m/smpte170m/bt709), 720x356 [SAR 32:27 DAR 640:267], 716 kb/s, 23.98 fps, 59.94 tbr, 90k tbn, 180k tbc (default)
  
      Metadata:
  
        creation_time   : 2014-10-20T13:01:06.000000Z
  
        encoder         : JVT/AVC Coding
  
      Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 159 kb/s (default)
  
      Metadata:
  
        creation_time   : 2014-10-20T13:01:06.000000Z
  
      Stream #0:2(eng): Audio: ac3 (ac-3 / 0x332D6361), 48000 Hz, 5.1(side), fltp, 448 kb/s
  
      Metadata:
  
        creation_time   : 2014-10-20T13:01:06.000000Z
  
      Side data:
  
        audio service type: main
  
      Stream #0:3(und): Data: bin_data (text / 0x74786574)
  
      Metadata:
  
        creation_time   : 2014-10-21T13:42:00.000000Z
  
      Stream #0:4: Video: png, rgb24(pc), 1400x2100, 90k tbr, 90k tbn, 90k tbc
  
  Unsupported codec with id 100359 for input stream 3
  
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  DISPOSITION:hearing_impaired=0
  
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  TAG:creation_time=2014-10-20T13:01:06.000000Z
  
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  [/STREAM]
  
  [STREAM]
  
  index=1
  
  codec_name=aac
  
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  profile=LC
  
  codec_type=audio
  
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  DISPOSITION:dub=0
  
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  TAG:creation_time=2014-10-20T13:01:06.000000Z
  
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  [/STREAM]
  
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  [/STREAM]

  Thanks. Brian

• Revision 30332 : On passe la génération de la file d’attente et donc des inserts en base ...

  29 juillet 2009, par kent1@… — Log

  On passe la génération de la file d’attente et donc des inserts en base dans l’action spipmotion_ajouter_file
  On ajoute un suffixe au nom de fichier -encoded pour éviter de boucler sur les encodages
  On clean aussi un chouilla

• Four Trends Shaping the Future of Analytics in Banking

  27 novembre 2024, par Daniel Crough — Banking and Financial Services

  While retail banking revenues have been growing in recent years, trends like rising financial crimes and capital required for generative AI and ML tech pose significant risks and increase operating costs across the financial industry, according to McKinsey’s State of Retail Banking report.

   

  Today’s financial institutions are focused on harnessing AI and advanced analytics to make their data work for them. To be up to the task, analytics solutions must allow banks to give consumers the convenient, personalised experiences they want while respecting their privacy.

   

  In this article, we’ll explore some of the big trends shaping the future of analytics in banking and finance. We’ll also look at how banks use data and technology to cut costs and personalise customer experiences.
  
  So, let’s get into it.

  

  This doesn’t just represent a security risk, it also impacts the usability for both customers and employees. Does any of the following sound familiar ?

  • Only specific senior employees know how to navigate the software to generate custom reports or use its more advanced features.
  • Customer complaints about your site’s usability or online banking experience are routine.
  • Onboarding employees takes much longer than necessary because of convoluted systems.
  • Teams and departments experience ‘data siloing,’ meaning that not everyone can access the data they need.

  These are warning signs that IT systems are ready for a review. Anyone thinking, “If it’s not broken, why fix it ?” should consider that legacy systems can also present data security risks. As more countries introduce regulations to protect customer privacy, staying ahead of the curve is increasingly important to avoid penalties and litigation.

  And regulations aren’t the only trends impacting the future of financial institutions’ IT and analytics.

  4 trends shaping the future of analytics in banking

  New regulations and new technology have changed the landscape of analytics in banking.

  New privacy regulations impact banks globally

  The first major international example was the advent of GDPR, which went into effect in the EU in 2018. But a lot has happened since. New privacy regulations and restrictions around AI continue to roll out.

  • The European Artificial Intelligence Act (EU AI Act), which was held up as the world’s first comprehensive legislation on AI, took effect on 31 July 2024.
  • In Europe’s federated data initiative, Gaia-X’s planned cloud infrastructure will provide for more secure, transparent, and trustworthy data storage and processing.
  • The revised Payment Services Directive (PSD2) makes payments more secure and strengthens protections for European businesses and consumers, aiming to create a more integrated and efficient payments market.

  But even businesses that don’t have customers in Europe aren’t safe. Consumer privacy is a hot-button issue globally.

  For example, the California Consumer Privacy Act (CCPA), which took effect in January, impacts the financial services industry more than any other. Case in point, 34% of CCPA-related cases filed in 2022 were related to the financial sector.

  California’s privacy regulations were the first in the US, but other states are following closely behind. On 1 July 2024, new privacy laws went into effect in Florida, Oregon, and Texas, giving people more control over their data.

  

  One typical issue for companies in the banking industry is that their privacy measures regarding user data collected from their website are much less lax than those in their online banking system.

  It’s better to proactively invest in a privacy-centric analytics platform before you get tangled up in a lawsuit and have to pay a fine (and are forced to change your system anyway). 

  And regulatory compliance isn’t the only bonus of an ethical analytics solution. The right alternative can unlock key customer insights that can help you improve the user experience.

  The demand for personalised banking services

  At the same time, consumers are expecting a more and more streamlined personal experience from financial institutions. 86% of bank employees say personalisation is a clear priority for the company. But 63% described resources as limited or only available after demonstrating clear business cases.

  McKinsey’s The data and analytics edge in corporate and commercial banking points out how advanced analytics are empowering frontline bank employees to give customers more personalised experiences at every stage :

  • Pre-meeting/meeting prep : Using advanced analytics to assess customer potential, recommend products, and identify prospects who are most likely to convert
  • Meetings/negotiation : Applying advanced models to support price negotiations, what-if scenarios and price multiple products simultaneously
  • Post-meeting/tracking : Using advanced models to identify behaviours that lead to high performance and improve forecast accuracy and sales execution

  Today’s banks must deliver the personalisation that drives customer satisfaction and engagement to outperform their competitors.

  The rise of AI and its role in banking

  With AI and machine learning technologies becoming more powerful and accessible, financial institutions around the world are already reaping the rewards.

  McKinsey estimates that AI in banking could add $200 to 340 billion annually across the global banking sector through productivity gains.

  • Credit card fraud prevention : Algorithms analyse usage to flag and block fraudulent transactions.
  • More accurate forecasting : AI-based tools can analyse a broader spectrum of data points and forecast more accurately.
  • Better risk assessment and modelling : More advanced analytics and predictive models help avoid extending credit to high-risk customers.
  • Predictive analytics : Help spot clients most likely to churn 
  • Gen-AI assistants : Instantly analyse customer profiles and apply predictive models to suggest the next best actions.

  Considering these market trends, let’s discuss how you can move your bank into the future.

  Using analytics to minimise risk and establish a competitive edge 

  With the right approach, you can leverage analytics and AI to help future-proof your bank against changing customer expectations, increased fraud, and new regulations.

  Use machine learning to prevent fraud

  Every year, more consumers are victims of credit and debit card fraud. Debit card skimming cases nearly doubled in the US in 2023. The last thing you want as a bank is to put your customer in a situation where a criminal has spent their money.

  This not only leads to a horrible customer experience but also creates a lot of internal work and additional costs.Thankfully, machine learning can help identify suspicious activity and stop transactions before they go through. For example, Mastercard’s fraud prevention model has improved fraud detection rates by 20–300%.

  

  Implementing a solution like this (or partnering with credit card companies who use it) may be a way to reduce risk and improve customer trust.

  Foresee and avoid future issues with AI-powered risk management

  Regardless of what type of financial products organisations offer, AI can be an enormous tool. Here are just a few ways in which it can mitigate financial risk in the future :

  • Predictive analytics can evaluate risk exposure and allow for more informed decisions about whether to approve commercial loan applications.
  • With better credit risk modelling, banks can avoid extending personal loans to customers most likely to default.
  • Investment banks (or individual traders or financial analysts) can use AI- and ML-based systems to monitor market and trading activity more effectively.

  Those are just a few examples that barely scratch the surface. Many other AI-based applications and analytics use cases exist across all industries and market segments.

  Protect customer privacy while still getting detailed analytics

  New regulations and increasing consumer privacy concerns don’t mean banks and financial institutions should forego website analytics altogether. Its insights into performance and customer behaviour are simply too valuable. And without customer interaction data, you’ll only know something’s wrong if someone complains.

  Fortunately, it doesn’t have to be one or the other. The right financial analytics solution can give you the data and insights needed without compromising privacy while complying with regulations like GDPR and CCPA.

  That way, you can track usage patterns and improve site performance and content quality based on accurate data — without compromising privacy. Reliable, precise analytics are crucial for any bank that’s serious about user experience.

  Use A/B testing and other tools to improve digital customer experiences

  Personalised digital experiences can be key differentiators in banking and finance when done well. But there’s stiff competition. In 2023, 40% of bank customers rated their bank’s online and mobile experience as excellent. 

  Improving digital experiences for users while respecting their privacy means going above and beyond a basic web analytics tool like Google Analytics. Invest in a platform with features like A/B tests and user session analysis for deeper insights into user behaviour.

  

  Behavioural analytics are crucial to understanding customer interactions. By identifying points of friction and drop-off points, you can make digital experiences smoother and more engaging.

  Matomo offers all this and is a great GDPR-compliant alternative to Google Analytics for banks and financial institutions. 

  Of course, this can be challenging. This is why taking an ethical and privacy-centric approach to analytics can be a key competitive edge for banks. Prioritising data security and privacy will attract other like-minded, ethically conscious consumers and boost customer loyalty.

  Get privacy-friendly web analytics suitable for banking & finance with Matomo

  Improving digital experiences for today’s customers requires a solid web analytics platform that prioritises data privacy and accurate analytics. And choosing the wrong one could even mean ending up in legal trouble or scrambling to reconstruct your entire analytics setup.

  Matomo provides privacy-friendly analytics with 100% data accuracy (no sampling), advanced privacy controls and the ability to run A/B tests and user session analysis within the same platform (limiting risk and minimising costs). 

  It’s easy to get started with Matomo. Users can access clear, easy-to-understand metrics and plenty of pre-made reports that deliver valuable insights from day one. Form usage reports can help banks and fintechs identify potential issues with broken links or technical glitches and reveal clues on improving UX in the short term.

  Over one million websites, including some of the world’s top banks and financial institutions, use Matomo for their analytics.

  Start your 21-day free trial to see why, or book a demo with one of our analytics experts.