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  • FFmpeg 5 : How to add cover art to m4a file

    17 mai 2022, par dch09

    I've tried

    


    ffmpeg -i input.m4a -i image.jpg -map 0 -map 1 -c copy -disposition:v:1 attached_pic output.m4a 


    


    from this answer FFmpeg, how to embed cover art (image) to .m4a

    


    but that does not seem to work correctly, giving me output as below :

    


        ffmpeg version 5.0.1 Copyright (c) 2000-2022 the FFmpeg developers
  built with Apple clang version 13.1.6 (clang-1316.0.21.2)
  configuration: --prefix=/opt/homebrew/Cellar/ffmpeg/5.0.1 --enable-shared --enable-pthreads --enable-version3 --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-librist --enable-librubberband --enable-libsnappy --enable-libsrt --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-neon
  libavutil      57. 17.100 / 57. 17.100
  libavcodec     59. 18.100 / 59. 18.100
  libavformat    59. 16.100 / 59. 16.100
  libavdevice    59.  4.100 / 59.  4.100
  libavfilter     8. 24.100 /  8. 24.100
  libswscale      6.  4.100 /  6.  4.100
  libswresample   4.  3.100 /  4.  3.100
  libpostproc    56.  3.100 / 56.  3.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/Users/username/Desktop/output.m4a':
  Metadata:
    major_brand     : M4A
    minor_version   : 512
    compatible_brands: M4A isomiso2
    encoder         : Lavf59.16.100
  Duration: 00:00:03.69, start: 0.000000, bitrate: 131 kb/s
  Stream #0:0[0x1](und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 127 kb/s (default)
    Metadata:
      handler_name    : SoundHandler
      vendor_id       : [0][0][0][0]
Input #1, image2, from '/Users/username/Pictures/image.jpg':
  Duration: 00:00:00.04, start: 0.000000, bitrate: 14753 kb/s
  Stream #1:0: Video: mjpeg (Progressive), yuvj420p(pc, bt470bg/unknown/unknown), 947x960 [SAR 1:1 DAR 947:960], 25 fps, 25 tbr, 25 tbn
[ipod @ 0x14df051e0] Could not find tag for codec mjpeg in stream #1, codec not currently supported in container
Could not write header for output file #0 (incorrect codec parameters ?): Invalid argument
Error initializing output stream 0:1 --
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
  Stream #1:0 -> #0:1 (copy)
    Last message repeated 1 times


    


  • FFMPEG image capture only executes partially on cron

    17 mai 2022, par C. Ardayfio

    I have the following shell script :

    


    DailyScript.sh

    


    #!/bin/sh
now="/Users/mydir/Downloads/LIDOPRO/Captures/$(date +'%Y_%m_%d_%I_%M_%p').jpg"
"/Users/mydir/Downloads/LIDOPRO/ffmpeg" -ss 0.5 -f avfoundation -framerate 30 -i "0" -frames:v 1 -t 1 "$now"


    


    It produces an image through my FaceTime camera when I run the script manually in shell, as follows :

    


    $ /Users/mydir/Downloads/LIDOPRO/DailyScript.sh


    


    And the output I get is the following

    


    ffmpeg version N-106916-ge71d5156c8-tessus Copyright (c) 2000-2022 the FFmpeg developers
  built with Apple clang version 11.0.0 (clang-1100.0.33.17)
  configuration: --cc=/usr/bin/clang --prefix=/opt/ffmpeg --extra-version=tessus --enable-avisynth --enable-fontconfig --enable-gpl --enable-libaom --enable-libass --enable-libbluray --enable-libdav1d --enable-libfreetype --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libmysofa --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvmaf --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-version3 --pkg-config-flags=--static --disable-ffplay
  libavutil      57. 24.101 / 57. 24.101
  libavcodec     59. 28.100 / 59. 28.100
  libavformat    59. 23.100 / 59. 23.100
  libavdevice    59.  6.100 / 59.  6.100
  libavfilter     8. 38.100 /  8. 38.100
  libswscale      6.  6.100 /  6.  6.100
  libswresample   4.  6.100 /  4.  6.100
  libpostproc    56.  5.100 / 56.  5.100
[avfoundation @ 0x7f88f3004f00] Selected pixel format (yuv420p) is not supported by the input device.
[avfoundation @ 0x7f88f3004f00] Supported pixel formats:
[avfoundation @ 0x7f88f3004f00]   uyvy422
[avfoundation @ 0x7f88f3004f00]   yuyv422
[avfoundation @ 0x7f88f3004f00]   nv12
[avfoundation @ 0x7f88f3004f00]   0rgb
[avfoundation @ 0x7f88f3004f00]   bgr0
[avfoundation @ 0x7f88f3004f00] Overriding selected pixel format to use uyvy422 instead.
0: could not seek to position 63537.297
Input #0, avfoundation, from '0':
  Duration: N/A, start: 63536.797200, bitrate: N/A
  Stream #0:0: Video: rawvideo (UYVY / 0x59565955), uyvy422, 640x480, 30 tbr, 1000k tbn
Stream mapping:
  Stream #0:0 -> #0:0 (rawvideo (native) -> mjpeg (native))
Press [q] to stop, [?] for help
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f8018000] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f8040000] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f8050000] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f8060000] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f8070000] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f8080000] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f8090000] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f80a0000] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x7f88f8008000] [swscaler @ 0x7f88f80b0000] deprecated pixel format used, make sure you did set range correctly
Output #0, image2, to '/Users/caineardayfio/Downloads/LIDOPRO/Captures/2022_05_17_12_57_PM.jpg':
  Metadata:
    encoder         : Lavf59.23.100
  Stream #0:0: Video: mjpeg, yuvj422p(pc, progressive), 640x480, q=2-31, 200 kb/s, 30 fps, 30 tbn
    Metadata:
      encoder         : Lavc59.28.100 mjpeg
    Side data:
      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: N/A
frame=    1 fps=0.0 q=5.2 Lsize=N/A time=00:00:00.06 bitrate=N/A speed=0.232x    
video:20kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown


    


    Everything works perfectly fine and the image is output.

    


    So, I created a cron job to run the script automatically, as follows :

    


    * * * * * /Users/caineardayfio/Downloads/LIDOPRO/DailyScript.sh 2> /tmp/error.txt 1> /tmp/output.txt


    


    However, this cronjob does not actually work. No photo is produced and the script seems to execute, but only partially :
error.txt

    


    ffmpeg version N-106916-ge71d5156c8-tessus Copyright (c) 2000-2022 the FFmpeg developers
  built with Apple clang version 11.0.0 (clang-1100.0.33.17)
  configuration: --cc=/usr/bin/clang --prefix=/opt/ffmpeg --extra-version=tessus --enable-avisynth --enable-fontconfig --enable-gpl --enable-libaom --enable-libass --enable-libbluray --enable-libdav1d --enable-libfreetype --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libmysofa --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvmaf --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-version3 --pkg-config-flags=--static --disable-ffplay
  libavutil      57. 24.101 / 57. 24.101
  libavcodec     59. 28.100 / 59. 28.100
  libavformat    59. 23.100 / 59. 23.100
  libavdevice    59.  6.100 / 59.  6.100
  libavfilter     8. 38.100 /  8. 38.100
  libswscale      6.  6.100 /  6.  6.100
  libswresample   4.  6.100 /  4.  6.100
  libpostproc    56.  5.100 / 56.  5.100
[avfoundation @ 0x7fe5af104580] Selected pixel format (yuv420p) is not supported by the input device.
[avfoundation @ 0x7fe5af104580] Supported pixel formats:
[avfoundation @ 0x7fe5af104580]   uyvy422
[avfoundation @ 0x7fe5af104580]   yuyv422
[avfoundation @ 0x7fe5af104580]   nv12
[avfoundation @ 0x7fe5af104580]   0rgb
[avfoundation @ 0x7fe5af104580]   bgr0
[avfoundation @ 0x7fe5af104580] Overriding selected pixel format to use uyvy422 instead.


    


    output.txt is empty

    


    Does anyone know how to resolve this issue ? I'd like the image to be produced.

    


  • How HSBC and ING are transforming banking with AI

    9 novembre 2024, par Daniel Crough — Banking and Financial Services, Featured Banking Content

    We recently partnered with FinTech Futures to produce an exciting webinar discussing how analytics leaders from two global banks are using AI to protect customers, streamline operations, and support environmental goals.

    Watch the on-demand webinar : Advancing analytics maturity.

    By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.





    <script>document.getElementById( "ak_js_3" ).setAttribute( "value", ( new Date() ).getTime() );</script>

    &lt;script&gt;<br />
    gform.initializeOnLoaded( function() {gformInitSpinner( 71, 'https://matomo.org/wp-content/plugins/gravityforms/images/spinner.svg', true );jQuery('#gform_ajax_frame_71').on('load',function(){var contents = jQuery(this).contents().find('*').html();var is_postback = contents.indexOf('GF_AJAX_POSTBACK') &gt;= 0;if(!is_postback){return;}var form_content = jQuery(this).contents().find('#gform_wrapper_71');var is_confirmation = jQuery(this).contents().find('#gform_confirmation_wrapper_71').length &gt; 0;var is_redirect = contents.indexOf('gformRedirect(){') &gt;= 0;var is_form = form_content.length &gt; 0 &amp;&amp; ! is_redirect &amp;&amp; ! is_confirmation;var mt = parseInt(jQuery('html').css('margin-top'), 10) + parseInt(jQuery('body').css('margin-top'), 10) + 100;if(is_form){jQuery('#gform_wrapper_71').html(form_content.html());if(form_content.hasClass('gform_validation_error')){jQuery('#gform_wrapper_71').addClass('gform_validation_error');} else {jQuery('#gform_wrapper_71').removeClass('gform_validation_error');}setTimeout( function() { /* delay the scroll by 50 milliseconds to fix a bug in chrome */  }, 50 );if(window['gformInitDatepicker']) {gformInitDatepicker();}if(window['gformInitPriceFields']) {gformInitPriceFields();}var current_page = jQuery('#gform_source_page_number_71').val();gformInitSpinner( 71, 'https://matomo.org/wp-content/plugins/gravityforms/images/spinner.svg', true );jQuery(document).trigger('gform_page_loaded', [71, current_page]);window['gf_submitting_71'] = false;}else if(!is_redirect){var confirmation_content = jQuery(this).contents().find('.GF_AJAX_POSTBACK').html();if(!confirmation_content){confirmation_content = contents;}setTimeout(function(){jQuery('#gform_wrapper_71').replaceWith(confirmation_content);jQuery(document).trigger('gform_confirmation_loaded', [71]);window['gf_submitting_71'] = false;wp.a11y.speak(jQuery('#gform_confirmation_message_71').text());}, 50);}else{jQuery('#gform_71').append(contents);if(window['gformRedirect']) {gformRedirect();}}jQuery(document).trigger(&quot;gform_pre_post_render&quot;, [{ formId: &quot;71&quot;, currentPage: &quot;current_page&quot;, abort: function() { this.preventDefault(); } }]);                if (event.defaultPrevented) {                return;         }        const gformWrapperDiv = document.getElementById( &quot;gform_wrapper_71&quot; );        if ( gformWrapperDiv ) {            const visibilitySpan = document.createElement( &quot;span&quot; );            visibilitySpan.id = &quot;gform_visibility_test_71&quot;;            gformWrapperDiv.insertAdjacentElement( &quot;afterend&quot;, visibilitySpan );        }        const visibilityTestDiv = document.getElementById( &quot;gform_visibility_test_71&quot; );        let postRenderFired = false;                function triggerPostRender() {            if ( postRenderFired ) {                return;            }            postRenderFired = true;            jQuery( document ).trigger( 'gform_post_render', [71, current_page] );            gform.utils.trigger( { event: 'gform/postRender', native: false, data: { formId: 71, currentPage: current_page } } );            if ( visibilityTestDiv ) {                visibilityTestDiv.parentNode.removeChild( visibilityTestDiv );            }        }        function debounce( func, wait, immediate ) {            var timeout;            return function() {                var context = this, args = arguments;                var later = function() {                    timeout = null;                    if ( !immediate ) func.apply( context, args );                };                var callNow = immediate &amp;&amp; !timeout;                clearTimeout( timeout );                timeout = setTimeout( later, wait );                if ( callNow ) func.apply( context, args );            };        }        const debouncedTriggerPostRender = debounce( function() {            triggerPostRender();        }, 200 );        if ( visibilityTestDiv &amp;&amp; visibilityTestDiv.offsetParent === null ) {            const observer = new MutationObserver( ( mutations ) =&gt; {                mutations.forEach( ( mutation ) =&gt; {                    if ( mutation.type === 'attributes' &amp;&amp; visibilityTestDiv.offsetParent !== null ) {                        debouncedTriggerPostRender();                        observer.disconnect();                    }                });            });            observer.observe( document.body, {                attributes: true,                childList: false,                subtree: true,                attributeFilter: [ 'style', 'class' ],            });        } else {            triggerPostRender();        }    } );} );<br />
    &lt;/script&gt;

    Meet the expert panel

    Roshini Johri heads ESG Analytics at HSBC, where she leads AI and remote sensing applications supporting the bank’s net zero goals. Her expertise spans climate tech and financial services, with a focus on scalable analytics solutions.

     

    Marco Li Mandri leads Advanced Analytics Strategy at ING, where he focuses on delivering high-impact solutions and strengthening analytics foundations. His background combines analytics, KYC operations, and AI strategy.

     

    Carmen Soini Tourres works as a Web Analyst Consultant at Matomo, helping financial organisations optimise their digital presence whilst maintaining privacy compliance.

     

    Key findings from the webinar

    The discussion highlighted four essential elements for advancing analytics capabilities :

    1. Strong data foundations matter most

    “It doesn’t matter how good the AI model is. It is garbage in, garbage out,”

    Johri explained. Banks need robust data governance that works across different regulatory environments.

    2. Transform rather than tweak

    Li Mandri emphasised the need to reconsider entire processes :

    “We try to look at the banking domain and processes and try to re-imagine how they should be done with AI.”

    3. Bridge technical and business understanding

    Both leaders stressed the value of analytics translators who understand both technology and business needs.

    “We’re investing in this layer we call product leads,”

    Li Mandri explained. These roles combine technical knowledge with business acumen – a rare but vital skill set.

    4. Consider production costs early

    Moving from proof-of-concept to production requires careful planning. As Johri noted :

    “The scale of doing things in production is quite massive and often doesn’t get accounted for in the cost.”

    This includes :

    • Ongoing monitoring requirements
    • Maintenance needs
    • Regulatory compliance checks
    • Regular model updates

    Real-world applications

    ING’s approach demonstrates how banks can transform their operations through thoughtful AI implementation. Li Mandri shared several areas where the bank has successfully deployed analytics solutions, each benefiting both the bank and its customers.

    Customer experience enhancement

    The bank’s implementation of AI-powered instant loan processing shows how analytics can transform traditional banking.

    “We know AI can make loans instant for the customer, that’s great. Clicking one button and adding a loan, that really changes things,”

    Li Mandri explained. This goes beyond automation – it represents a fundamental shift in how banks serve their customers.

    The system analyses customer data to make rapid lending decisions while maintaining strong risk assessment standards. For customers, this means no more lengthy waiting periods or complex applications. For the bank, it means more efficient resource use and better risk management.

    The bank also uses AI to personalise customer communications.

    “We’re using that to make certain campaigns more personalised, having a certain tone of voice,”

    noted Li Mandri. This particularly resonates with younger customers who expect relevant, personalised interactions from their bank.

    Operational efficiency transformation

    ING’s approach to Know Your Customer (KYC) processes shows how AI can transform resource-heavy operations.

    “KYC is a big area of cost for the bank. So we see massive value there, a lot of scale,”

    Li Mandri explained. The bank developed an AI-powered system that :

    • Automates document verification
    • Flags potential compliance issues for human review
    • Maintains consistent standards across jurisdictions
    • Reduces processing time while improving accuracy

    This implementation required careful consideration of regulations across different markets. The bank developed monitoring systems to ensure their AI models maintain high accuracy while meeting compliance standards.

    In the back office, ING uses AI to extract and process data from various documents, significantly reducing manual work. This automation lets staff focus on complex tasks requiring human judgment.

    Sustainable finance initiatives

    ING’s commitment to sustainable banking has driven innovative uses of AI in environmental assessment.

    “We have this ambition to be a sustainable bank. If you want to be a sustainable finance customer, that requires a lot of work to understand who the company is, always comparing against its peers.”

    The bank developed AI models that :

    • Analyse company sustainability metrics
    • Compare environmental performance against industry benchmarks
    • Assess transition plans for high-emission industries
    • Monitor ongoing compliance with sustainability commitments

    This system helps staff evaluate the environmental impact of potential deals quickly and accurately.

    “We are using AI there to help our frontline process customers to see how green that deal might be and then use that as a decision point,”

    Li Mandri noted.

    HSBC’s innovative approach

    Under Johri’s leadership, HSBC has developed several groundbreaking uses of AI and analytics, particularly in environmental monitoring and operational efficiency. Their work shows how banks can use advanced technology to address complex global challenges while meeting regulatory requirements.

    Environmental monitoring through advanced technology

    HSBC uses computer vision and satellite imagery analysis to measure environmental impact with new precision.

    “This is another big research area where we look at satellite images and we do what is called remote sensing, which is the study of a remote area,”

    Johri explained.

    The system provides several key capabilities :

    • Analysis of forest coverage and deforestation rates
    • Assessment of biodiversity impact in specific regions
    • Monitoring of environmental changes over time
    • Measurement of environmental risk in lending portfolios

    “We can look at distant images of forest areas and understand how much percentage deforestation is being caused in that area, and we can then measure our biodiversity impact more accurately,”

    Johri noted. This technology enables HSBC to :

    • Make informed lending decisions
    • Monitor environmental commitments of borrowers
    • Support sustainability-linked lending programmes
    • Provide accurate environmental impact reporting

    Transforming document analysis

    HSBC is tackling one of banking’s most time-consuming challenges : processing vast amounts of documentation.

    “Can we reduce the onus of human having to go and read 200 pages of sustainability reports each time to extract answers ?”

    Johri asked. Their solution combines several AI technologies to make this process more efficient while maintaining accuracy.

    The bank’s approach includes :

    • Natural language processing to understand complex documents
    • Machine learning models to extract relevant information
    • Validation systems to ensure accuracy
    • Integration with existing compliance frameworks

    “We’re exploring solutions to improve our reporting, but we need to do it in a safe, robust and transparent way.”

    This careful balance between efficiency and accuracy exemplifies HSBC’s approach to AI.

    Building future-ready analytics capabilities

    Both banks emphasise that successful analytics requires a comprehensive, long-term approach. Their experiences highlight several critical considerations for financial institutions looking to advance their analytics capabilities.

    Developing clear governance frameworks

    “Understanding your AI risk appetite is crucial because banking is a highly regulated environment,”

    Johri emphasised. Banks need to establish governance structures that :

    • Define acceptable uses for AI
    • Establish monitoring and control mechanisms
    • Ensure compliance with evolving regulations
    • Maintain transparency in AI decision-making

    Creating solutions that scale

    Li Mandri stressed the importance of building systems that grow with the organisation :

    “When you try to prototype a model, you have to take care about the data safety, ethical consideration, you have to identify a way to monitor that model. You need model standard governance.”

    Successful scaling requires :

    • Standard approaches to model development
    • Clear evaluation frameworks
    • Simple processes for model updates
    • Strong monitoring systems
    • Regular performance reviews

    Investing in people and skills

    Both leaders highlighted how important skilled people are to analytics success.

    “Having a good hiring strategy as well as creating that data literacy is really important,”

    Johri noted. Banks need to :

    • Develop comprehensive training programmes
    • Create clear career paths for analytics professionals
    • Foster collaboration between technical and business teams
    • Build internal expertise in emerging technologies

    Planning for the future

    Looking ahead, both banks are preparing for increased regulation and growing demands for transparency. Key focus areas include :

    • Adapting to new privacy regulations
    • Making AI decisions more explainable
    • Improving data quality and governance
    • Strengthening cybersecurity measures

    Practical steps for financial institutions

    The experiences shared by HSBC and ING provide valuable insights for financial institutions at any stage of their analytics journey. Their successes and challenges outline a clear path forward.

    Key steps for success

    Financial institutions looking to enhance their analytics capabilities should :

    1. Start with strong foundations
      • Invest in clear data governance frameworks
      • Set data quality standards
      • Build thorough documentation processes
      • Create transparent data tracking
    2. Think strategically about AI implementation
      • Focus on transformative rather than small changes
      • Consider the full costs of AI projects
      • Build solutions that can grow
      • Balance innovation with risk management
    3. Invest in people and processes
      • Develop internal analytics expertise
      • Create clear paths for career growth
      • Foster collaboration between technical and business teams
      • Build a culture of data literacy
    4. Plan for scale
      • Establish monitoring systems
      • Create governance frameworks
      • Develop standard approaches to model development
      • Stay flexible for future regulatory changes

    Learn more

    Want to hear more insights from these industry leaders ? Watch the complete webinar recording on demand. You’ll learn :

    • Detailed technical insights from both banks
    • Extended Q&A with the speakers
    • Additional case studies and examples
    • Practical implementation advice
     
     

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    Watch the on-demand webinar : Advancing analytics maturity.

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