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• How to Use Analytics & Reports for Marketing, Sales & More

  28 septembre 2023, par Erin — Analytics Tips

  By now, most professionals know they should be using analytics and reports to make better business decisions. Blogs and thought leaders talk about it all the time. But most sources don’t tell you how to use analytics and reports. So marketers, salespeople and others either skim whatever reports they come across or give up on making data-driven decisions entirely. 

  But it doesn’t have to be this way.

  In this article, we’ll cover what analytics and reports are, how they differ and give you examples of each. Then, we’ll explain how clean data comes into play and how marketing, sales, and user experience teams can use reports and analytics to uncover actionable insights.

  What’s the difference between analytics & reports ? 

  Many people speak of reports and analytics as if the terms are interchangeable, but they have two distinct meanings.

  A report is a collection of data presented in one place. By tracking key metrics and providing numbers, reports tell you what is happening in your business. Analytics is the study of data and the process of generating insights from data. Both rely on data and are essential for understanding and improving your business results.

  

  A science experiment is a helpful analogy for how reporting and analytics work together. To conduct an experiment, scientists collect data and results and compile a report of what happened. But the process doesn’t stop there. After generating a data report, scientists analyse the data and try to understand the why behind the results.

  In a business context, you collect and organise data in reports. With analytics, you then use those reports and their data to draw conclusions about what works and what doesn’t.

  Reports examples 

  Reports are a valuable tool for just about any part of your business, from sales to finance to human resources. For example, your finance team might collect data about spending and use it to create a report. It might show how much you spend on employee compensation, real estate, raw materials and shipping.

  On the other hand, your marketing team might benefit from a report on lead sources. This would mean collecting data on where your sales leads come from (social media, email, organic search, etc.). You could collect and present lead source data over time for a more in-depth report. This shows which sources are becoming more effective over time. With advanced tools, you can create detailed, custom reports that include multiple factors, such as time, geographical location and device type.

  

  Analytics examples 

  Because analytics requires looking at and drawing insights from data and reports to collect and present data, analytics often begins by studying reports. 

  In our example of a report on lead sources, an analytics professional might study the report and notice that webinars are an important source of leads. To better understand this, they might look closely at the number of leads acquired compared to how often webinars occur. If they notice that the number of webinar leads has been growing, they might conclude that the business should invest in more webinars to generate more leads. This is just one kind of insight analytics can provide.

  For another example, your human resources team might study a report on employee retention. After analysing the data, they could discover valuable insights, such as which teams have the highest turnover rate. Further analysis might help them uncover why certain teams fail to keep employees and what they can do to solve the problem.

  The importance of clean data 

  Both analytics and reporting rely on data, so it’s essential your data is clean. Clean data means you’ve audited your data, removed inaccuracies and duplicate entries, and corrected mislabelled data or errors. Basically, you want to ensure that each piece of information you’re using for reports and analytics is accurate and organised correctly.

  If your data isn’t clean and accurate, neither will your reports be. And making business decisions based on bad data can come at a considerable cost. Inaccurate data might lead you to invest in a channel that appears more valuable than it actually is. Or it could cause you to overlook opportunities for growth. Moreover, poor data maintenance and the poor insight it provides will lead your team to have less trust in your reports and analytics team.

  The simplest way to maintain clean data is to be meticulous when inputting or transferring data. This can be as simple as ensuring that your sales team fills in every field of an account record. When you need to import or transfer data from other sources, you need to perform quality assurance (QA) checks to make sure data is appropriately labelled and organised. 

  Another way to maintain clean data is by avoiding cookies. Most web visitors reject cookie consent banners. When this happens, analysts and marketers don’t get data on these visitors and only see the percentage of users who accept tracking. This means they decide on a smaller sample size, leading to poor or inaccurate data. These banners also create a poor user experience and annoy web visitors.

  Matomo can be configured to run cookieless — which, in most countries, means you don’t need to have an annoying cookie consent screen on your site. This way, you can get more accurate data and create a better user experience.

  Marketing analytics and reports 

  Analytics and reporting help you measure and improve the effectiveness of your marketing efforts. They help you learn what’s working and what you should invest more time and money into. And bolstering the effectiveness of your marketing will create more opportunities for sales.

  

  One common area where marketing teams use analytics and reports is to understand and improve their keyword rankings and search engine optimization. They use web analytics platforms like Matomo to report on how their website performs for specific keywords. Insights from these reports are then used to inform changes to the website and the development of new content.

  As we mentioned above, marketing teams often use reports on lead sources to understand how their prospects and customers are learning about the brand. They might analyse their lead sources to better understand their audience. 

  For example, if your company finds that you receive a lot of leads from LinkedIn, you might decide to study the content you post there and how it differs from other platforms. You could apply a similar content approach to other channels to see if it increases lead generation. You can then study reporting on how lead source data changes after you change content strategies. This is one example of how analysing a report can lead to marketing experimentation. 

  Email and paid advertising are also marketing channels that can be optimised with reports and analysis. By studying the data around what emails and ads your audience clicks on, you can draw insights into what topics and messaging resonate with your customers.

  

  Marketing teams often use A/B testing to learn about audience preferences. In an A/B test, you can test two landing page versions, such as two different types of call-to-action (CTA) buttons. Matomo will generate a report showing how many people clicked each version. From those results, you may draw an insight into the design your audience prefers.

  Sales analytics and reports 

  Sales analytics and reports are used to help teams close more deals and sell more efficiently. They also help businesses understand their revenue, set goals, and optimise sales processes. And understanding your sales and revenue allows you to plan for the future.

  One of the keys to building a successful sales strategy and team is understanding your sales cycle. That’s why it’s so important for companies to analyse their lead and sales data. For business-to-business (B2B) companies in particular, the sales cycle can be a long process. But you can use reporting and analytics to learn about the stages of the buying cycle, including how long they take and how many leads proceed to the next step.

  Analysing lead and customer data also allows you to gain insights into who your customers are. With detailed account records, you can track where your customers are, what industries they come from, what their role is and how much they spend. While you can use reports to gather customer data, you also have to use analysis and qualitative information in order to build buyer personas. 

  

  Many sales teams use past individual and business performance to understand revenue trends. For instance, you might study historical data reports to learn how seasonality affects your revenue. If you dive deeper, you might find that seasonal trends may depend on the country where your customers live. 

  

  Conversely, it’s also important to analyse what internal variables are affecting revenue. You can use revenue reports to identify your top-performing sales associates. You can then try to expand and replicate that success. While sales is a field often driven by personal relationships and conversations, many types of reports allow you to learn about and improve the process.

  Website and user behaviour analytics and reports 

  More and more, businesses view their websites as an experience and user behaviour as an important part of their business. And just like sales and marketing, reporting and analytics help you better understand and optimise your web experience. 

  Many web and user behaviour metrics, like traffic source, have important implications for marketing. For example, page traffic and user flows can provide valuable insights into what your customers are interested in. This can then drive future content development and marketing campaigns.

  

  You can also learn about how your users navigate and use your website. A robust web analytics tool, like Matomo, can supply user session recordings and visitor tracking. For example, you could study which pages a particular user visits. But Matomo also has a feature called Transitions that provides visual reports showing where a particular page’s traffic comes from and where visitors tend to go afterward. 

  

  As you consider why people might be leaving your website, site performance is another important area for reporting. Most users are accustomed to near-instantaneous web experiences, so it’s worth monitoring your page load time and looking out for backend delays. In today’s world, your website experience is part of what you’re selling to customers. Don’t miss out on opportunities to impress and delight them.

  Dive into your data

  Reporting and analytics can seem like mysterious buzzwords we’re all supposed to understand already. But, like anything else, they require definitions and meaningful examples. When you dig into the topic, though, the applications for reporting and analytics are endless.

  Use these examples to identify how you can use analytics and reports in your role and department to achieve better results, whether that means higher quality leads, bigger deal size or a better user experience.

  To see how Matomo can collect accurate and reliable data and turn it into in-depth analytics and reports, start a free 21-day trial. No credit card required.

• ffmpeg giving Error while decoding stream #0:1 : Invalid data found when processing input

  11 octobre 2023, par user1432181

  I am trying to merge two video files into one using ffmpeg on Windows. The process has been proven to work over and over (with over 100 files merged together at some points) - but I have come across an input file that is causing the process to fail with the errors :

  


  _[aac @ 00000142532f74c0] channel element 1.0 is not allocated
Error while decoding stream #0:1: Invalid data found when processing input
[aac @ 00000142532f74c0] channel element 1.0 is not allocated
Error while decoding stream #0:1: Invalid data found when processing input
[aac @ 00000142532f74c0] channel element 1.0 is not allocated
.
.
.


  


  There seems to be 3 command line steps to get here, using a concats-inputs.dat file containing :

  


  file E:/..../snippet A.mp4
file E:/..../snippet B.mp4


  


  (Copies of these files can be found at https://filebin.net/77wbowvh7vbklkey/snippet_A.mp4 and https://filebin.net/77wbowvh7vbklkey/snippet_B.mp4)

  


  Step 1 :

  


  > ffmpeg-6.0-full_build/bin/ffmpeg -y -progress ".Default.mp4.progressinfo.dat" -vsync 0 -f concat -safe 0 -i "E:/...../concat-inputs.dat" -c:v copy -c:a copy -crf 0 -b:v 10M "E:/...../video.Default.mp4"


  


  with the output....

  


  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)

  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint

  libavutil      58.  2.100 / 58.  2.100

  libavcodec     60.  3.100 / 60.  3.100

  libavformat    60.  3.100 / 60.  3.100

  libavdevice    60.  1.100 / 60.  1.100

  libavfilter     9.  3.100 /  9.  3.100

  libswscale      7.  1.100 /  7.  1.100

  libswresample   4. 10.100 /  4. 10.100

  libpostproc    57.  1.100 / 57.  1.100

-vsync is deprecated. Use -fps_mode

Passing a number to -vsync is deprecated, use a string argument as described in the manual.

[mov,mp4,m4a,3gp,3g2,mj2 @ 000001bf88ffe240] Auto-inserting h264_mp4toannexb bitstream filter

Input #0, concat, from 'E:/...../concat-inputs.dat':

  Duration: N/A, start: -0.010667, bitrate: 20382 kb/s

  Stream #0:0(und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], 20043 kb/s, 50 fps, 50 tbr, 12800 tbn

    Metadata:

      handler_name    : VideoHandler

      vendor_id       : [0][0][0][0]

      encoder         : Lavc60.3.100 libx264

  Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 96000 Hz, 5.1, fltp, 339 kb/s

    Metadata:

      handler_name    : SoundHandler

      vendor_id       : [0][0][0][0]

Output #0, mp4, to 'E:/...../video.Default.mp4':

  Metadata:

    encoder         : Lavf60.3.100

  Stream #0:0(und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 10000 kb/s, 50 fps, 50 tbr, 12800 tbn

    Metadata:

      handler_name    : VideoHandler

      vendor_id       : [0][0][0][0]

      encoder         : Lavc60.3.100 libx264

  Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 96000 Hz, 5.1, fltp, 339 kb/s

    Metadata:

      handler_name    : SoundHandler

      vendor_id       : [0][0][0][0]

Stream mapping:

  Stream #0:0 -> #0:0 (copy)

  Stream #0:1 -> #0:1 (copy)

Press [q] to stop, [?] for help

frame=    0 fps=0.0 q=-1.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=N/A    
_[mov,mp4,m4a,3gp,3g2,mj2 @ 000001bf890653c0] Auto-inserting h264_mp4toannexb bitstream filter

[mp4 @ 000001bf89000580] Non-monotonous DTS in output stream 0:1; previous: 180224, current: 180192; changing to 180225. This may result in incorrect timestamps in the output file.

frame=  210 fps=0.0 q=-1.0 Lsize=   11537kB time=00:00:04.21 bitrate=22433.7kbits/s speed=41.9x

video:11417kB audio:114kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.053312%


  


  Step 2

  


  > ffmpeg-6.0-full_build/bin/ffmpeg -y -progress ".Default.mp4.progressinfo.dat" -vsync 0 -f concat -safe 0 -i "E:/...../concat-inputs.dat" -c:v copy -c:a copy -crf 0 -b:v 10M "E:/...../audio.Default.wav"


  


  which outputs...

  


  ffmpeg version 6.0-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers

  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)

  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint

  libavutil      58.  2.100 / 58.  2.100

  libavcodec     60.  3.100 / 60.  3.100

  libavformat    60.  3.100 / 60.  3.100

  libavdevice    60.  1.100 / 60.  1.100

  libavfilter     9.  3.100 /  9.  3.100

  libswscale      7.  1.100 /  7.  1.100

  libswresample   4. 10.100 /  4. 10.100

  libpostproc    57.  1.100 / 57.  1.100

-vsync is deprecated. Use -fps_mode

Passing a number to -vsync is deprecated, use a string argument as described in the manual.

[mov,mp4,m4a,3gp,3g2,mj2 @ 00000246d314e240] Auto-inserting h264_mp4toannexb bitstream filter

Input #0, concat, from 'E:/...../concat-inputs.dat':

  Duration: N/A, start: -0.010667, bitrate: 20382 kb/s

  Stream #0:0(und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], 20043 kb/s, 50 fps, 50 tbr, 12800 tbn

    Metadata:

      handler_name    : VideoHandler

      vendor_id       : [0][0][0][0]

      encoder         : Lavc60.3.100 libx264

  Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 96000 Hz, 5.1, fltp, 339 kb/s

    Metadata:

      handler_name    : SoundHandler

      vendor_id       : [0][0][0][0]

[out#0/wav @ 00000246d31bd240] Codec AVOption b (set bitrate (in bits/s)) has not been used for any stream. The most likely reason is either wrong type (e.g. a video option with no video streams) or that it is a private option of some encoder which was not actually used for any stream.

Output #0, wav, to 'E:/...../audio.Default.wav':

  Metadata:

    ISFT            : Lavf60.3.100

  Stream #0:0(und): Audio: aac (LC) ([255][0][0][0] / 0x00FF), 96000 Hz, 5.1, fltp, 339 kb/s

    Metadata:

      handler_name    : SoundHandler

      vendor_id       : [0][0][0][0]

Stream mapping:

  Stream #0:1 -> #0:0 (copy)

Press [q] to stop, [?] for help

size=       0kB time=00:00:00.00 bitrate=N/A speed=N/A    
_[mov,mp4,m4a,3gp,3g2,mj2 @ 00000246d3b009c0] Auto-inserting h264_mp4toannexb bitstream filter

[wav @ 00000246d3150580] Non-monotonous DTS in output stream 0:0; previous: 180224, current: 180192; changing to 180224. This may result in incorrect timestamps in the output file.

size=     114kB time=00:00:04.21 bitrate= 222.4kbits/s speed= 128x

video:0kB audio:114kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.102561%


  


  Step 3

  


  > ffmpeg-6.0-full_build/bin/ffmpeg -y -progress ".Default.mp4.progressinfo.dat" -i "E:/...../video.Default.mp4" -i "E:/...../audio.Default.wav" -crf 0 -c:v copy -c:a aac "E:/...../Default.mp4"


  


  ... which then gives the errors....

  


  ffmpeg version 6.0-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers

  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)

  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint

  libavutil      58.  2.100 / 58.  2.100

  libavcodec     60.  3.100 / 60.  3.100

  libavformat    60.  3.100 / 60.  3.100

  libavdevice    60.  1.100 / 60.  1.100

  libavfilter     9.  3.100 /  9.  3.100

  libswscale      7.  1.100 /  7.  1.100

  libswresample   4. 10.100 /  4. 10.100

  libpostproc    57.  1.100 / 57.  1.100

Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'E:/...../video.Default.mp4':

  Metadata:

    major_brand     : isom

    minor_version   : 512

    compatible_brands: isomiso2avc1mp41

    encoder         : Lavf60.3.100

  Duration: 00:00:04.23, start: 0.000000, bitrate: 22359 kb/s

  Stream #0:0[0x1](und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], 22178 kb/s, 49.80 fps, 50 tbr, 12800 tbn (default)

    Metadata:

      handler_name    : VideoHandler

      vendor_id       : [0][0][0][0]

      encoder         : Lavc60.3.100 libx264

  Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 96000 Hz, 5.1, fltp, 221 kb/s (default)

    Metadata:

      handler_name    : SoundHandler

      vendor_id       : [0][0][0][0]

[aac @ 000001425315e580] Multiple frames in a packet.

Input #1, wav, from 'E:/...../audio.Default.wav':

  Metadata:

    encoder         : Lavf60.3.100

  Duration: 00:00:04.22, bitrate: 221 kb/s

  Stream #1:0: Audio: aac (LC) ([255][0][0][0] / 0x00FF), 96000 Hz, 5.1, fltp, 339 kb/s

Stream mapping:

  Stream #0:0 -> #0:0 (copy)

  Stream #0:1 -> #0:1 (aac (native) -> aac (native))

Press [q] to stop, [?] for help

Output #0, mp4, to 'E:/...../Default.mp4':

  Metadata:

    major_brand     : isom

    minor_version   : 512

    compatible_brands: isomiso2avc1mp41

    encoder         : Lavf60.3.100

  Stream #0:0(und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 22178 kb/s, 49.80 fps, 50 tbr, 12800 tbn (default)

    Metadata:

      handler_name    : VideoHandler

      vendor_id       : [0][0][0][0]

      encoder         : Lavc60.3.100 libx264

  Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 96000 Hz, 5.1, fltp, 341 kb/s (default)

    Metadata:

      handler_name    : SoundHandler

      vendor_id       : [0][0][0][0]

      encoder         : Lavc60.3.100 aac

frame=    0 fps=0.0 q=-1.0 size=       0kB time=-577014:32:22.77 bitrate=  -0.0kbits/s speed=N/A    
_[aac @ 00000142532f74c0] channel element 1.0 is not allocated

Error while decoding stream #0:1: Invalid data found when processing input

[aac @ 00000142532f74c0] channel element 1.0 is not allocated
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  If I was to do this to merge snippet B with snippet B then it would work - it's something about snippet A that is causing the problem.

  


  Is there any way to get around this... what is it about snippet A that is causing a problem... and is there a way to "normalize" it so that it can be merged as part of the "set".

  


  Note, I just upgraded to ffmpeg6 after a previous version was giving the same problems - so I will also work on the deprecated messages when I can.

  


• FFMPEG changes pixel values when reading and saving png without modification

  25 janvier 2023, par walrus

  This is a toy problem that is the result of my trying to identify a bug within a video pipeline I'm working on. The idea is that I want to take a frame from a YUV420 video, modify it as an RGB24 image, and reinsert it. To do this I convert YUV420 -> YUV444 -> RGB -> YUV444 -> YUV420. Doing this without any modification should result in the same frame however I noticed slight color transformations.

  


  I tried to isolate the problem using a toy 3x3 RGB32 png image. The function read_and_save_image reads the image and then saves it as new file. It returns the read pixel array. I run this function thrice successively using the output of the previous run as the input of the next. This is to demonstrate a perplexing fact. While passing an image through the function once causes the resulting image to have different pixel values, doing it twice does not change anything. Perhaps more confusing is that the pixel values returned by the function are all the same.

  


  tldr ; How can I load and save the toy image below using ffmpeg as a new file such that the pixel values of the new and original files are identical ?

  


  Here is the original image followed by the result from one and two passes through the function. Note that the pixel value displayed by when reading these images with Preview has changed ever so slightly. This becomes noticeable within a video.

  


  Test image (very small) ->&#xA; <-

  &#xA;

  Here are the pixel values read (note that after being loaded and saved there is a change) :

  &#xA;

  original test image

  &#xA;

  test image after one pass

  &#xA;

  test image after two passes

  &#xA;

  Edit : here is an RGB24 frame extracted from a video I am using to test my pipeline. I had the same issue with pixel values changing after loading and saving with ffmpeg.

  &#xA;

  frame from video I was testing pipeline on

  &#xA;

  Here is a screenshot showing how the image is noticeably darker after ffmpeg. Same pixels on the top right corner of the image.

  &#xA;

  

  &#xA;

  Here is the code of the toy problem :

  &#xA;

  import os
import ffmpeg
import numpy as np


def read_and_save_image(in_file, out_file, width, height, pix_fmt='rgb32'):
    input_data, _ = (
        ffmpeg
        .input(in_file)
        .output('pipe:', format='rawvideo', pix_fmt=pix_fmt)
        .run(capture_stdout=True)
    )
  
    frame = np.frombuffer(input_data, np.uint8)
    print(in_file,'\n', frame.reshape((height,width,-1)))
    
    save_data = (
        ffmpeg
            .input('pipe:', format='rawvideo', pix_fmt=pix_fmt, s='{}x{}'.format(width, height))
            .output(out_file, pix_fmt=pix_fmt)
            .overwrite_output()
            .run_async(pipe_stdin=True)
    )
    
    

    save_data.stdin.write(frame.tobytes())
    save_data.stdin.close()
    #save_data.wait()

    return frame

try:
    test_img = "test_image.png"
    test_img_1 = "test_image_1.png"
    test_img_2 = "test_image_2.png"
    test_img_3 = "test_image_3.png"

    width, height, pix_fmt = 3,3,'rgb32'
    #width, height, pix_fmt = video_stream['width'], video_stream['height'],  'rgb24'
    test_img_pxls = read_and_save_image(test_img,test_img_1, width, height, pix_fmt)
    test_img_1_pxls = read_and_save_image(test_img_1,test_img_2, width, height, pix_fmt)
    test_img_2_pxls = read_and_save_image(test_img_2,test_img_3, width, height, pix_fmt)

    print(np.array_equiv(test_img_pxls, test_img_1_pxls))
    print(np.array_equiv(test_img_1_pxls, test_img_2_pxls))

except ffmpeg.Error as e:
    print('stdout:', e.stdout.decode('utf8'))
    print('stderr:', e.stderr.decode('utf8'))
    raise e


!mediainfo --Output=JSON --Full $test_img
!mediainfo --Output=JSON --Full $test_img_1
!mediainfo --Output=JSON --Full $test_img_2


  &#xA;

  Here is the console output of the program that shows that the pixel arrays read by ffmpeg are the same despite the images being different.

  &#xA;

  test_image.png 
 [[[253 218 249 255]
  [252 213 248 255]
  [251 200 244 255]]

 [[253 227 250 255]
  [249 209 236 255]
  [243 169 206 255]]

 [[253 235 251 255]
  [245 195 211 255]
  [226 103 125 255]]]
test_image_1.png 
 [[[253 218 249 255]
  [252 213 248 255]
  [251 200 244 255]]

 [[253 227 250 255]
  [249 209 236 255]
  [243 169 206 255]]

 [[253 235 251 255]
  [245 195 211 255]
  [226 103 125 255]]]
test_image_2.png 
 [[[253 218 249 255]
  [252 213 248 255]
  [251 200 244 255]]

 [[253 227 250 255]
  [249 209 236 255]
  [243 169 206 255]]

 [[253 235 251 255]
  [245 195 211 255]
  [226 103 125 255]]]
True
True
{
"media": {
"@ref": "test_image.png",
"track": [
{
"@type": "General",
"ImageCount": "1",
"FileExtension": "png",
"Format": "PNG",
"FileSize": "4105",
"StreamSize": "0",
"File_Modified_Date": "UTC 2023-01-19 13:49:00",
"File_Modified_Date_Local": "2023-01-19 13:49:00"
},
{
"@type": "Image",
"Format": "PNG",
"Format_Compression": "LZ77",
"Width": "3",
"Height": "3",
"BitDepth": "32",
"Compression_Mode": "Lossless",
"StreamSize": "4105"
}
]
}
}

{
"media": {
"@ref": "test_image_1.png",
"track": [
{
"@type": "General",
"ImageCount": "1",
"FileExtension": "png",
"Format": "PNG",
"FileSize": "128",
"StreamSize": "0",
"File_Modified_Date": "UTC 2023-01-24 15:31:58",
"File_Modified_Date_Local": "2023-01-24 15:31:58"
},
{
"@type": "Image",
"Format": "PNG",
"Format_Compression": "LZ77",
"Width": "3",
"Height": "3",
"BitDepth": "32",
"Compression_Mode": "Lossless",
"StreamSize": "128"
}
]
}
}

{
"media": {
"@ref": "test_image_2.png",
"track": [
{
"@type": "General",
"ImageCount": "1",
"FileExtension": "png",
"Format": "PNG",
"FileSize": "128",
"StreamSize": "0",
"File_Modified_Date": "UTC 2023-01-24 15:31:59",
"File_Modified_Date_Local": "2023-01-24 15:31:59"
},
{
"@type": "Image",
"Format": "PNG",
"Format_Compression": "LZ77",
"Width": "3",
"Height": "3",
"BitDepth": "32",
"Compression_Mode": "Lossless",
"StreamSize": "128"
}
]
}
}



  &#xA;