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• ffmpeg framemd5 errors with FFV1.mkv in Windows

  19 août 2022, par jim_e_jib

  I'm using framemd5 to check lossless transcoding to FFV1.mkv and am getting mismatches between my source and FFV1 files.

  


  If I generate the framemd5 files in Windows, I get presentation timestamp mismatches on the audio between the source file and FFV1. The MD5s themselves match.

  


  If I generate the framemd5 files for the FFV1 in Linux (WSL), everything matches just fine.

  


  Is there something I am doing wrong, or is this just a quirk of the ffmpeg/FFV1/Windows combination ?

  


  I am using ffmpeg version 2022-07-14-git-882aac99d2-full_build-www.gyan.dev in Windows and
ffmpeg version 4.4.2-0ubuntu1 20.04.sav2 in WSL.

  


  Many thanks !

  


  Jim

  


• Data Privacy in Business : A Risk Leading to Major Opportunities

  9 août 2022, par Erin — Privacy

  Data privacy in business is a contentious issue. 

  Claims that “big data is the new oil of the digital economy” and strong links between “data-driven personalisation and customer experience” encourage leaders to set up massive data collection programmes.

  However, many of these conversations downplay the magnitude of security, compliance and ethical risks companies face when betting too much on customer data collection. 

  In this post, we discuss the double-edged nature of privacy issues in business — the risk-ridden and the opportunity-driven. ​​

  3 Major Risks of Ignoring Data Privacy in Business

  As the old adage goes : Just because everyone else is doing it doesn’t make it right.

  Easy data accessibility and ubiquity of analytics tools make data consumer collection and processing sound like a “given”. But the decision to do so opens your business to a spectrum of risks. 

  1. Compliance and Legal Risks 

  Data collection and customer privacy are protected by a host of international laws including GDPR, CCPA, and regional regulations. Only 15% of countries (mostly developing ones) don’t have dedicated laws for protecting consumer privacy. 

  

  Global legislature includes provisions on : 

  • Collectible data types
  • Allowed uses of obtained data 
  • Consent to data collection and online tracking 
  • Rights to request data removal 

  Personally identifiable information (PII) processing is prohibited or strictly regulated in most jurisdictions. Yet businesses repeatedly circumnavigate existing rules and break them on occasion.

  In Australia, for example, only 2% of brands use logos, icons or messages to transparently call out online tracking, data sharing or other specific uses of data at the sign-up stage. In Europe, around half of small businesses are still not fully GDPR-compliant — and Big Tech companies like Google, Amazon and Facebook can’t get a grip on their data collection practices even when pressed with horrendous fines. 

  Although the media mostly reports on compliance fines for “big names”, smaller businesses are increasingly receiving more scrutiny. 

  As Max Schrems, an Austrian privacy activist and founder of noyb NGO, explained in a Matomo webinar :

  “In Austria, my home country, there are a lot of €5,000 fines going out there as well [to smaller businesses]. Most of the time, they are just not reported. They just happen below the surface. [GDPR fines] are already a reality.”​

  In April 2022, the EU Court of Justice ruled that consumer groups can autonomously sue businesses for breaches of data protection — and nonprofit organisations like noyb enable more people to do so. 

  Finally, new data privacy legislation is underway across the globe. In the US, Colorado, Connecticut, Virginia and Utah have data protection acts at different stages of approval. South African authorities are working on the Protection of Personal Information Act (POPI) act and Brazil is working on a local General Data Protection Law (LGPD).

  Re-thinking your stance on user privacy and data protection now can significantly reduce the compliance burden in the future. 

  2. Security Risks 

  Data collection also mandates data protection for businesses. Yet, many organisations focus on the former and forget about the latter. 

  Lenient attitudes to consumer data protection resulted in a major spike in data breaches.

  Check Point research found that cyberattacks increased 50% year-over-year, with each organisation facing 925 cyberattacks per week globally.

  Many of these attacks end up being successful due to poor data security in place. As a result, billions of stolen consumer records become publicly available or get sold on dark web marketplaces.

  What’s even more troublesome is that stolen consumer records are often purchased by marketing firms or companies, specialising in spam campaigns. Buyers can also use stolen emails to distribute malware, stage phishing and other social engineering attacks – and harvest even more data for sale. 

  One business’s negligence creates a snowball effect of negative changes down the line with customers carrying the brunt of it all. 

  In 2020, hackers successfully targeted a Finnish psychotherapy practice. They managed to steal hundreds of patient records — and then demanded a ransom both from the firm and its patients for not exposing information about their mental health issues. Many patients refused to pay hackers and some 300 records ended up being posted online as Associated Press reported.

  Not only did the practice have to deal with the cyber-breach aftermath, but it also faced vocal regulatory and patient criticisms for failing to properly protect such sensitive information.

  Security negligence can carry both direct (heavy data breach fines) and indirect losses in the form of reputational damages. An overwhelming 90% of consumers say they wouldn’t buy from a business if it doesn’t adequately protect their data. This brings us to the last point. 

  3. Reputational Risks 

  Trust is the new currency. Data negligence and consumer privacy violations are the two fastest ways to lose it. 

  Globally, consumers are concerned about how businesses collect, use, and protect their data. 

  

  • According to Forrester, 47% of UK adults actively limit the amount of data they share with websites and apps. 49% of Italians express willingness to ask companies to delete their personal data. 36% of Germans use privacy and security tools to minimise online tracking of their activities. 
  • A GDMA survey also notes that globally, 82% of consumers want more control over their personal information, shared with companies. 77% also expect brands to be transparent about how their data is collected and used. 

  When businesses fail to hold their end of the bargain — collect just the right amount of data and use it with integrity — consumers are fast to cut ties. 

  Once the information about privacy violations becomes public, companies lose : 

  • Brand equity 
  • Market share 
  • Competitive positioning 

  An AON report estimates that post-data breach companies can lose as much as 25% of their initial value. In some cases, the losses can be even higher. 

  In 2015, British telecom TalkTalk suffered from a major data breach. Over 150,000 customer records were stolen by hackers. To contain the issue, TalkTalk had to throw between $60-$70 million into containment efforts. Still, they lost over 100,000 customers in a matter of months and one-third of their company value, equivalent to $1.4 billion, by the end of the year. 

  Fresher data from Infosys gives the following maximum cost estimates of brand damage, companies could experience after a data breach (accidental or malicious).

  

  3 Major Advantages of Privacy in Business 

  Despite all the industry mishaps, a reassuring 77% of CEOs now recognise that their companies must fundamentally change their approaches to customer engagement, in particular when it comes to ensuring data privacy. 

  Many organisations take proactive steps to cultivate a privacy-centred culture and implement transparent data collection policies. 

  Here’s why gaining the “privacy advantage” pays off.

  1. Market Competitiveness 

  There’s a reason why privacy-focused companies are booming. 

  Consumers’ mounting concerns and frustrations over the lack of online privacy, prompt many to look for alternative privacy-centred products and services. 

  The following B2C and B2B products are moving from the industry margins to the mainstream : 

  • Private search engines (Duckduckgo)
  • Password managers (1password, Dashlane)
  • Online identity networks (id.me)
  • Web analytics solutions (Matomo) 
  • And secure messaging apps (Signal) among others 

  Across the board, consumers express greater trust towards companies, protective of their privacy : 

  

  And as we well know : trust translates to higher engagement, loyalty, and – ultimately revenue. 

  By embedding privacy into the core of your product, you give users more reasons to select, stay and support your business. 

  2. Higher Operational Efficiency

  Customer data protection isn’t just a policy – it’s a culture of collecting “just enough” data, protecting it and using it responsibly. 

  Sadly, that’s the area where most organisations trail behind. At present, some 90% of businesses admit to having amassed massive data silos. 

  Siloed data is expensive to maintain and operationalise. Moreover, when left unattended, it can evolve into a pressing compliance issue. 

  A recently leaked document from Facebook says the company has no idea where all of its first-party, third-party and sensitive categories data goes or how it is processed. Because of this, Facebook struggles to achieve GDPR compliance and remains under regulatory pressure. 

  Similarly, Google Analytics is riddled with privacy issues. Other company products were found to be collecting and operationalising consumer data without users’ knowledge or consent. Again, this creates valid grounds for regulatory investigations. 

  Smaller companies have a better chance of making things right at the onset. 

  By curbing customer data collection, you can : 

  • Reduce data hosting and Cloud computation costs (aka trim your Cloud bill) 
  • Improve data security practices (since you would have fewer assets to protect) 
  • Make your staff more productive by consolidating essential data and making it easy and safe to access

  Privacy-mindful companies also have an easier time when it comes to compliance and can meet new data regulations faster. 

  3. Better Marketing Campaigns 

  The biggest counter-argument to reducing customer data collection is marketing. 

  How can we effectively sell our products if we know nothing about our customers ? – your team might be asking. 

  This might sound counterintuitive, but minimising data collection and usage can lead to better marketing outcomes. 

  Limiting the types of data that can be used encourages your people to become more creative and productive by focusing on fewer metrics that are more important.

  Think of it this way : Every other business uses the same targeting parameters on Facebook or Google for running paid ad campaigns on Facebook. As a result, we see ads everywhere — and people grow unresponsive to them or choose to limit exposure by using ad blocking software, private browsers and VPNs. Your ad budgets get wasted on chasing mirage metrics instead of actual prospects. 

  Case in point : In 2017 Marc Pritchard of Procter & Gamble decided to first cut the company’s digital advertising budget by 6% (or $200 million). Unilever made an even bolder move and reduced its ad budget by 30% in 2018. 

  Guess what happened ?

  P&G saw a 7.5% increase in organic sales and Unilever had a 3.8% gain as HBR reports. So how come both companies became more successful by spending less on advertising ? 

  They found that overexposure to online ads led to diminishing returns and annoyances among loyal customers. By minimising ad exposure and adopting alternative marketing strategies, the two companies managed to market better to new and existing customers. 

  The takeaway : There are more ways to engage consumers aside from pestering them with repetitive retargeting messages or creepy personalisation. 

  You can collect first-party data with consent to incrementally improve your product — and educate them on the benefits of your solution in transparent terms.

  Final Thoughts 

  The definitive advantage of privacy is consumers’ trust. 

  You can’t buy it, you can’t fake it, you can only cultivate it by aligning your external appearances with internal practices. 

  Because when you fail to address privacy internally, your mishaps will quickly become apparent either as social media call-outs or worse — as a security incident, a data breach or a legal investigation. 

  By choosing to treat consumer data with respect, you build an extra layer of protection around your business, plus draw in some banging benefits too. 

  Get one step closer to becoming a privacy-centred company by choosing Matomo as your web analytics solution. We offer robust privacy controls for ensuring ethical, compliant, privacy-friendly and secure website tracking. 

• FFmpeg saturates memory + CPU usage drops to 0% during very basic conversion of PNG files to MP4 video

  7 août 2022, par mattze_frisch

  I have this Python function that runs ffmpeg with minimal options from the Windows command line :

  


  def run_ffmpeg(frames_path, ffmpeg_path=notebook_directory):
    '''
    This function runs ffmpeg.exe to convert PNG image files into a MP4 video.
    
    Parameters
    ----------
    frames_path : string
        Absolute path to the PNG files
    ffmpeg_path : string
        Absolute path to the FFmpeg executable (ffmpeg.exe)
    '''
    
    from subprocess import check_call
    
    
    check_call(
        [
            os.path.join(ffmpeg_path, 'ffmpeg'),
            '-y',    # Overwrite output files without asking
            '-report',    # Write logfile to current working directory
            '-framerate', '60',    # Input frame rate
            '-i', os.path.join(frames_path, 'frame%05d.png'),    # Path to input frames
            os.path.join(frames_path, 'video.mp4')    # Path to store output video
        ]
    )


  


  When running it from a Jupyter notebook over 2500 PNG files (RGBA, ca. 600-700 kB each, 9000 x 13934 pixels), CPU usage briefly peaks to 100% before dropping to 0%, while memory usage quickly saturates to 100% and stays there, slowing the system down almost to a freeze, so I need to terminate ffmpeg from the task manager :

  


  Screenshot

  


  The generated video file has a size of only 48 bytes and contains just a black frame when viewed in the VLC player.

  


  This is the ffmpeg log output :

  


  ffmpeg started on 2022-08-05 at 17:17:55
Report written to "ffmpeg-20220805-171755.log"
Log level: 48
Command line:
"C:\\Users\\Username\\Desktop\\folder\\ffmpeg" -y -report -framerate 60 -i "C:\\Users\\Username\\Desktop\\e\\frame%05d.png" "C:\\Users\\Username\\Desktop\\e\\video.mp4"
ffmpeg version 2022-07-14-git-882aac99d2-full_build-www.gyan.dev Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 12.1.0 (Rev2, 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-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-libmfx --enable-libshaderc --enable-vulkan --enable-libplacebo --ena  libavutil      57. 29.100 / 57. 29.100
  libavcodec     59. 38.100 / 59. 38.100
  libavformat    59. 28.100 / 59. 28.100
  libavdevice    59.  8.100 / 59.  8.100
  libavfilter     8. 45.100 /  8. 45.100
  libswscale      6.  8.100 /  6.  8.100
  libswresample   4.  8.100 /  4.  8.100
  libpostproc    56.  7.100 / 56.  7.100
Splitting the commandline.
Reading option '-y' ... matched as option 'y' (overwrite output files) with argument '1'.
Reading option '-report' ... matched as option 'report' (generate a report) with argument '1'.
Reading option '-framerate' ... matched as AVOption 'framerate' with argument '60'.
Reading option '-i' ... matched as input url with argument 'C:\Users\Username\Desktop\e\frame%05d.png'.
Reading option 'C:\Users\Username\Desktop\e\video.mp4' ... matched as output url.
Finished splitting the commandline.
Parsing a group of options: global .
Applying option y (overwrite output files) with argument 1.
Applying option report (generate a report) with argument 1.
Successfully parsed a group of options.
Parsing a group of options: input url C:\Users\Username\Desktop\e\frame%05d.png.
Successfully parsed a group of options.
Opening an input file: C:\Users\Username\Desktop\e\frame%05d.png.
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00000.png' for reading
[file @ 0000000000425680] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042d800] Statistics: 668318 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00001.png' for reading
[file @ 000000000042dac0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042d6c0] Statistics: 668371 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00002.png' for reading
[file @ 000000000042d6c0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042dac0] Statistics: 669177 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00003.png' for reading
[file @ 000000000042dac0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000437a40] Statistics: 684594 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00004.png' for reading
[file @ 0000000000437a40] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000437c00] Statistics: 703014 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00005.png' for reading
[file @ 0000000000437c00] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000437d00] Statistics: 721604 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00006.png' for reading
[file @ 0000000000437cc0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000437f40] Statistics: 739761 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00007.png' for reading
[file @ 0000000000437f40] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000438040] Statistics: 757327 bytes read, 0 seeks
[image2 @ 000000000041ff80] Probe buffer size limit of 5000000 bytes reached
Input #0, image2, from 'C:\Users\Username\Desktop\e\frame%05d.png':
  Duration: 00:00:41.67, start: 0.000000, bitrate: N/A
  Stream #0:0, 8, 1/60: Video: png, rgba(pc), 9000x13934 [SAR 29528:29528 DAR 4500:6967], 60 fps, 60 tbr, 60 tbn
Successfully opened the file.
Parsing a group of options: output url C:\Users\Username\Desktop\e\video.mp4.
Successfully parsed a group of options.
Opening an output file: C:\Users\Username\Desktop\e\video.mp4.
[file @ 000000002081e3c0] Setting default whitelist 'file,crypto,data'
Successfully opened the file.
detected 12 logical cores
Stream mapping:
  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
Press [q] to stop, [?] for help
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00008.png' for reading
[file @ 00000000024ad980] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 00000000004379c0] Statistics: 767857 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00009.png' for reading
[file @ 000000000042d600] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 00000000004379c0] Statistics: 774848 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00010.png' for reading
[file @ 00000000004379c0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042da00] Statistics: 787178 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00011.png' for reading
[file @ 00000000004379c0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042da00] Statistics: 797084 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00012.png' for reading
[file @ 0000000000437a80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042da00] Statistics: 802870 bytes read, 0 seeks
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'video_size' to value '9000x13934'
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'pix_fmt' to value '26'
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'time_base' to value '1/60'
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'pixel_aspect' to value '29528/29528'
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'frame_rate' to value '60/1'
[graph 0 input from stream 0:0 @ 00000000208bf800] w:9000 h:13934 pixfmt:rgba tb:1/60 fr:60/1 sar:29528/29528
[format @ 00000000025ef840] Setting 'pix_fmts' to value 'yuv420p|yuvj420p|yuv422p|yuvj422p|yuv444p|yuvj444p|nv12|nv16|nv21|yuv420p10le|yuv422p10le|yuv444p10le|nv20le|gray|gray10le'
[auto_scale_0 @ 00000000025efe40] w:iw h:ih flags:'' interl:0
[format @ 00000000025ef840] auto-inserting filter 'auto_scale_0' between the filter 'Parsed_null_0' and the filter 'format'
[AVFilterGraph @ 000000000042da00] query_formats: 4 queried, 3 merged, 1 already done, 0 delayed
[auto_scale_0 @ 00000000025efe40] picking yuv444p out of 13 ref:rgba alpha:1
[auto_scale_0 @ 00000000025efe40] w:9000 h:13934 fmt:rgba sar:29528/29528 -> w:9000 h:13934 fmt:yuv444p sar:1/1 flags:0x0
[auto_scale_0 @ 00000000025efe40] w:9000 h:13934 fmt:rgba sar:29528/29528 -> w:9000 h:13934 fmt:yuv444p sar:1/1 flags:0x0
[auto_scale_0 @ 00000000025efe40] w:9000 h:13934 fmt:rgba sar:29528/29528 -> w:9000 h:13934 fmt:yuv444p sar:1/1 flags:0x0
[auto_scale_0 @ 00000000025efe40] w:9000 h:13934 fmt:rgba sar:29528/29528 -> w:9000 h:13934 fmt:yuv444p sar:1/1 flags:0x0
[libx264 @ 000000002081d280] using mv_range_thread = 376
[libx264 @ 000000002081d280] using SAR=1/1
[libx264 @ 000000002081d280] frame MB size (563x871) > level limit (139264)
[libx264 @ 000000002081d280] DPB size (4 frames, 1961492 mbs) > level limit (1 frames, 696320 mbs)
[libx264 @ 000000002081d280] MB rate (29422380) > level limit (16711680)
[libx264 @ 000000002081d280] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
[libx264 @ 000000002081d280] profile High 4:4:4 Predictive, level 6.2, 4:4:4, 8-bit
[libx264 @ 000000002081d280] 264 - core 164 r3095 baee400 - H.264/MPEG-4 AVC codec - Copyleft 2003-2022 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=18 lookahead_threads=3 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'C:\Users\Username\Desktop\e\video.mp4':
  Metadata:
    encoder         : Lavf59.28.100
  Stream #0:0, 0, 1/15360: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 9000x13934 [SAR 1:1 DAR 4500:6967], q=2-31, 60 fps, 15360 tbn
    Metadata:
      encoder         : Lavc59.38.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
Clipping frame in rate conversion by 0.000008
frame=    1 fps=0.8 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00013.png' for reading
[file @ 000000000a6a2180] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 810395 bytes read, 0 seeks
frame=    2 fps=0.8 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00014.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 818213 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00015.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 817936 bytes read, 0 seeks
frame=    4 fps=1.2 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00016.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 817014 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00017.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 828088 bytes read, 0 seeks
frame=    6 fps=1.5 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00018.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 831007 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00019.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 845203 bytes read, 0 seeks
frame=    8 fps=1.7 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00020.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 851548 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00021.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 847629 bytes read, 0 seeks
frame=   10 fps=1.8 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00022.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 860169 bytes read, 0 seeks
frame=   11 fps=1.4 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00023.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 857243 bytes read, 0 seeks
frame=   12 fps=1.2 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00024.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 835155 bytes read, 0 seeks


  


  What is the problem ?

  


  By the way, the color model of the image files was confirmed by doing

  


  from PIL import Image


img = Image.open('C:\\Users\\EPI-SMLM\\Desktop\\e\\frame00000.png')
img.mode
-------------------------------------------------------------------
C:\Program Files\Python38\lib\site-packages\PIL\Image.py:3035: DecompressionBombWarning: Image size (125406000 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.
  warnings.warn(

'RGBA'


  


  The "decompression bomb warning" appears to be a false alarm/bug.

  


  UPDATE : I can confirm that this happens even when there are only 50 image files, i.e. 50 x 700 kB = 35 MB in total size. ffmpeg still gobbles up all available memory (almost 60 GB of private bytes !!!).

  


  And it also happens if ffmpeg is run from the command line.

  


  This must be a bug !