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• FFMPEG command runs in terminal but not by subprocess

  1er septembre 2022, par Basilique

  I am trying to run a bash command using the subprocess module from within python 3.10.

  


  The bash command is :

  


  ffmpeg -framerate 1 -pattern_type glob -i '*.png' -c:v libx264 -pix_fmt yuv420p -vf "crop=trunc(iw/2)*2:trunc(ih/2)*2" out.mp4


  


  In terminal the command runs fine. Here is the output :

  


  ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)
  configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
Input #0, image2, from '*.png':
  Duration: 00:16:39.00, start: 0.000000, bitrate: N/A
    Stream #0:0: Video: png, rgba(pc), 895x332 [SAR 3937:3937 DAR 895:332], 1 fps, 1 tbr, 1 tbn, 1 tbc
Stream mapping:
  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0x55726ab95d00] using SAR=1/1
[libx264 @ 0x55726ab95d00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512
[libx264 @ 0x55726ab95d00] profile High, level 2.2
[libx264 @ 0x55726ab95d00] 264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - 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=-2 threads=10 lookahead_threads=1 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=1 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 'out.mp4':
  Metadata:
    encoder         : Lavf58.29.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 894x332 [SAR 1:1 DAR 447:166], q=-1--1, 1 fps, 16384 tbn, 1 tbc
    Metadata:
      encoder         : Lavc58.54.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
frame=  173 fps=0.0 q=17.0 size=     512kB time=00:01:56.00 bitrate=  36.2kbits/frame=  351 fps=350 q=17.0 size=    1536kB time=00:04:54.00 bitrate=  42.8kbits/frame=  517 fps=343 q=17.0 size=    2560kB time=00:07:40.00 bitrate=  45.6kbits/frame=  725 fps=361 q=17.0 size=    3328kB time=00:11:08.00 bitrate=  40.8kbits/frame=  913 fps=364 q=17.0 size=    4352kB time=00:14:16.00 bitrate=  41.6kbits/frame=  999 fps=361 q=-1.0 Lsize=    4986kB time=00:16:36.00 bitrate=  41.0kbits/s speed= 360x    
video:4974kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.241361%
[libx264 @ 0x55726ab95d00] frame I:4     Avg QP: 6.12  size: 24072
[libx264 @ 0x55726ab95d00] frame P:346   Avg QP:12.94  size:  5708
[libx264 @ 0x55726ab95d00] frame B:649   Avg QP:18.19  size:  4655
[libx264 @ 0x55726ab95d00] consecutive B-frames:  5.8% 16.0% 20.1% 58.1%
[libx264 @ 0x55726ab95d00] mb I  I16..4: 59.1% 10.6% 30.4%
[libx264 @ 0x55726ab95d00] mb P  I16..4:  5.6%  0.6%  2.2%  P16..4: 10.5%  4.3%  2.3%  0.0%  0.0%    skip:74.5%
[libx264 @ 0x55726ab95d00] mb B  I16..4:  2.2%  0.1%  1.7%  B16..8: 16.9%  4.8%  1.6%  direct: 1.1%  skip:71.5%  L0:50.9% L1:45.2% BI: 3.9%
[libx264 @ 0x55726ab95d00] 8x8 transform intra:5.9% inter:10.4%
[libx264 @ 0x55726ab95d00] coded y,uvDC,uvAC intra: 20.1% 18.3% 17.3% inter: 4.7% 4.7% 4.6%
[libx264 @ 0x55726ab95d00] i16 v,h,dc,p: 66% 33%  1%  0%
[libx264 @ 0x55726ab95d00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 18%  8% 73%  0%  0%  0%  0%  0%  0%
[libx264 @ 0x55726ab95d00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 31% 31%  2%  3%  2%  4%  2%  3%
[libx264 @ 0x55726ab95d00] i8c dc,h,v,p: 73% 23%  3%  0%
[libx264 @ 0x55726ab95d00] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x55726ab95d00] ref P L0: 57.2%  1.5% 24.3% 17.0%
[libx264 @ 0x55726ab95d00] ref B L0: 69.6% 24.8%  5.6%
[libx264 @ 0x55726ab95d00] ref B L1: 92.4%  7.6%
[libx264 @ 0x55726ab95d00] kb/s:40.78


  


  In my python script I tried the following solutions :

  


  video_cmd = """ffmpeg -framerate 1 -pattern_type glob -i '*.png' -c:v libx264 -pix_fmt yuv420p -vf "crop=trunc(iw/2)*2:trunc(ih/2)*2" out.mp4"""

subprocess.run(shlex.split(video_cmd), shell=False, cwd=path_viz, stderr=subprocess.STDOUT, check=True, text=False)

subprocess.run(video_cmd, shell=True, cwd=path_viz, stderr=subprocess.STDOUT, check=True, text=False)


  


  as well as the solution proposed for this similar question

  


  subprocess.Popen(video_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)


  


  None of them worked. Apparently, the right command is run (output of the check_out function) :

  


  Command 'ffmpeg -y -framerate 1 -pattern_type glob -i '*.png' -c:v libx264 -pix_fmt yuv420p -vf "crop=trunc(iw/2)*2:trunc(ih/2)*2" out.mp4' returned non-zero exit status 1.


  


  the first part of the job (up to Stream mapping:) is done also correctly :

  


  fmpeg version 4.3 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 7.3.0 (crosstool-NG 1.23.0.449-a04d0)
  configuration: --prefix=/home/rsghazanfari/anaconda3/envs/_cuda --cc=/opt/conda/conda-bld/ffmpeg_1597178665428/_build_env/bin/x86_64-conda_cos6-linux-gnu-cc --disable-doc --disable-openssl --enable-avresample --enable-gnutls --enable-hardcoded-tables --enable-libfreetype --enable-libopenh264 --enable-pic --enable-pthreads --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libmp3lame
  libavutil      56. 51.100 / 56. 51.100
  libavcodec     58. 91.100 / 58. 91.100
  libavformat    58. 45.100 / 58. 45.100
  libavdevice    58. 10.100 / 58. 10.100
  libavfilter     7. 85.100 /  7. 85.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  7.100 /  5.  7.100
  libswresample   3.  7.100 /  3.  7.100
Input #0, image2, from '*.png':
  Duration: 00:16:39.00, start: 0.000000, bitrate: N/A
    Stream #0:0: Video: png, rgba(pc), 895x332 [SAR 3937:3937 DAR 895:332], 1 fps, 1 tbr, 1 tbn, 1 tbc


  


  but it then pops up the following error :

  


  Unknown encoder &#x27;libx264&#x27;&#xA;Traceback (most recent call last):&#xA;  File "/home/rsgh/anaconda3/envs/_cuda/lib/python3.10/code.py", line 90, in runcode&#xA;    exec(code, self.locals)&#xA;  File "<input />", line 1, in <module>&#xA;  File "/home/rsgh/anaconda3/envs/_cuda/lib/python3.10/subprocess.py", line 524, in run&#xA;    raise CalledProcessError(retcode, process.args,&#xA;&#xA;subprocess.CalledProcessError: Command &#x27;ffmpeg -y -framerate 1 -pattern_type glob -i &#x27;*.png&#x27; -c:v libx264 -pix_fmt yuv420p -vf "crop=trunc(iw/2)*2:trunc(ih/2)*2" out.mp4&#x27; returned non-zero exit status 1.&#xA;</module>

  


  Any ideas of why this error is produced in python while in terminal it runs fine ? Thank you in advance.

  


  PS : ffmpeg -version outputs :

  


  ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers
built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)
configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
libavutil      56. 31.100 / 56. 31.100
libavcodec     58. 54.100 / 58. 54.100
libavformat    58. 29.100 / 58. 29.100
libavdevice    58.  8.100 / 58.  8.100
libavfilter     7. 57.100 /  7. 57.100
libavresample   4.  0.  0 /  4.  0.  0
libswscale      5.  5.100 /  5.  5.100
libswresample   3.  5.100 /  3.  5.100
libpostproc    55.  5.100 / 55.  5.100


  


  ubuntu version :

  


  Distributor ID: Ubuntu
Description:    Ubuntu 20.04.4 LTS
Release:    20.04
Codename:   focal


  


• Privacy in Business : What Is It and Why Is It Important ?

  13 juillet 2022, par Erin — Privacy

  Privacy concerns loom large among consumers. Yet, businesses remain reluctant to change the old ways of doing things until they become an operational nuisance. 

  More and more businesses are slowly starting to feel the pressure to incorporate privacy best practices. But what exactly does privacy mean in business ? And why is it important for businesses to protect users’ privacy ? 

  In this blog, we’ll answer all of these questions and more. 

  What is Privacy in Business ?

  In the corporate world, privacy stands for the business decision to use collected consumer data in a safe, secure and compliant way. 

  Companies with a privacy-centred culture : 

  • Get explicit user consent to tracking, opt-ins and data sharing 
  • Collect strictly necessary data in compliance with regulations 
  • Ask for permissions to collect, process and store sensitive data 
  • Provide transparent explanations about data operationalisation and usage 
  • Have mechanisms for data collection opt-outs and data removal requests 
  • Implement security controls for storing collected data and limit access permissions to it 

  In other words : They treat consumers’ data with utmost integrity and security – and provide reassurances of ethical data usage. 

  What Are the Ethical Business Issues Related to Privacy ?

  Consumer data analytics has been around for decades. But digital technologies – ubiquitous connectivity, social media networks, data science and machine learning – increased the magnitude and sophistication of customer profiling.

  Big Tech companies like Google and Facebook, among others, capture millions of data points about users. These include general demographics data like “age” or “gender”, as well as more granular insights such as “income”, “past browsing history” or “recently visited geo-locations”. 

  When combined, such personally identifiable information (PII) can be used to approximate the user’s exact address, frequently purchased goods, political beliefs or past medical conditions. Then such information is shared with third parties such as advertisers. 

  That’s when ethical issues arise. 

  The Cambridge Analytica data scandal is a prime example of consumer data that was unethically exploited. 

  Over the years, Google also faced a series of regulatory issues surrounding consumer privacy breaches :

  • In 2021, a Google Chrome browser update put some 2.6 billion users at risk of “surveillance, manipulation and abuse” by providing third parties with data on device usage. 
  • The same year, Google was taken to court for failing to provide full disclosures on tracking performed in Google Chrome incognito mode. A $5 billion lawsuit is still pending.
  • As of 2022, Google Analytics 4 is considered GDPR non-compliant and was branded “illegal” by several European countries. 

  If you are curious, learn more about Google Analytics privacy issues. 

  The bigger issue ? Big Tech companies make the businesses that use their technologies (unknowingly) complicit in consumer data violations.

  In 2022, the Belgian data regulator found the official IAB Europe framework for user consent gathering in breach of GDPR. The framework was used by all major AdTech platforms to issue pop-ups for user consent to tracking. Now ad platforms must delete all data gathered through these. Biggest advertisers such as Procter & Gamble, Unilever, IBM and Mastercard among others, also received a notice about data removal and a regulatory warning on further repercussions if they fail to comply. 

  Big Tech firms have given brands unprecedented access to granular consumer data. Unrestricted access, however, also opened the door to data abuse and unethical use. 

  Examples of Unethical Data Usage by Businesses 

  • Data hoarding means excessively harvesting all available consumer data because a possibility to do so exists, often using murky consent mechanisms. Yet, 85% of collected Big Data is either dark or redundant, obsolete or trivial (ROT).
  • Invasive personalisation based on sensitive user information (or second-guesses), like a recent US marketing campaign, congratulating women on pregnancy (even if they weren’t expecting). Overall, 75% of consumers find most forms of personalisation somewhat creepy. 22% also said they’d leave for another brand due to creepy experiences.
  • Hyper-targeted advertising campaigns based on data consumers would prefer not to share. A recent investigation found that advertising platforms often assign sensitive labels to users (as part of their ad profiles), indicative of their religion, mental issues, history with abuse and so on. This allows advertisers to target such consumers with dubious ads. 

  Ultimately, excessive data collection, paired with poor data protection in business settings, results in major data breaches and costly damage control. Given that cyber attacks are on the rise, every business is vulnerable. 

  Why Should a Business Be Concerned About Protecting the Privacy of Its Customers ?

  Businesses must prioritise customer privacy because that’s what is expected of them. Globally, 89% of consumers say they care about their privacy. 

  As frequent stories about unethical data usage, excessive tracking and data breaches surface online, even more grow more concerned about protecting their data. Many publicly urge companies to take action. Others curtail their relationships with brands privately. 

  

  On average, 45% of consumers feel uncomfortable about sharing personal data. According to KPMG, 78% of American consumers have fears about the amount of data being collected. 40% of them also don’t trust companies to use their data ethically. Among Europeans, 41% are unwilling to share any personal data with businesses. 

  Because the demand for online privacy is rising, progressive companies now treat privacy as a competitive advantage. 

  For example, the encrypted messaging app Signal gained over 42 million active users in a year because it offers better data security and privacy protection. 

  ProtonMail, a privacy-centred email client, also amassed a 50 million user base in several years thanks to a “fundamentally stronger definition of privacy”.

  The growth of privacy-mindful businesses speaks volumes. And even more good things happen to privacy-mindful businesses : 

  • Higher consumer trust and loyalty 
  • Improved attractiveness to investors
  • Less complex compliance
  • Minimum cybersecurity exposure 
  • Better agility and innovation

  It’s time to start pursuing them ! Learn how to embed privacy and security into your operations.

• 4 Ways to Embed User Privacy & Data Security in Your Business

  15 juillet 2022, par Erin — Privacy

  Customer analytics undeniably plays a vital role for businesses. Product improvements, interface personalisation, content improvements, and creative advertising thrive on data. 

  Yet, there’s a fine line between being a customer-centred company and a privacy-violating one. 

  Due to ubiquitous online tracking, 62% of Americans now believe that it’s impossible to go about their daily lives without companies collecting data about them. Still, despite the importance of privacy in business for consumers, companies are reluctant to act. Privacy initiatives often stay on the back burner due to perceived complexity. That’s true to some extent.

  Privacy in business does assume complex technical changes to your data management. But to be a privacy-centred organisation, you also need to re-think your processes, practices, and culture. 

  Here are four ways to start your journey to better user privacy and data security. 

  1. Revise Your Data Collection Process to Gain Consumer Trust 

  The public is wary of sharing data with businesses because they are suspicious of its subsequent usage. 

  However, not all data collection is bad or wrong. In many cases, you need specific data for service delivery, compliance, or good-natured personalisation. 

  That’s exactly what consumers expect. Almost half of US consumers say they’d trust a company that limits the amount of personal information requested and only asks for data relevant to its products/services. 

  By limiting data collection and offering transparent data usage terms, you can : 

  • Reassure reluctant users to try your product or service — hence, boost conversions and sales. 
  • Retain existing audiences by gaining their trust, which leads to loyalty and higher customer lifetime value (CLV). 

  To gain consumers’ trust, implement proper consent and opt-out mechanisms. Then create educational materials about how you are collecting and using their data.

  2. Perform Data Mapping to Determine Where Sensitive Data Rests 

  Businesses are already pressed with an expanded cyber-security radar, courtesy of remote work, digital payment processing, IoT device adoption, etc. Yet, 41% of the executives don’t think their security initiatives have kept up with the digital transformations.

  Loopholes in security eventually result in a data breach. The average cost of a data breach looms at $4.24 million globally. The sum includes regulatory fines and containment costs, plus indirect losses in the form of reduced brand equity and market share. 

  Lax data protection in business also undermines consumer trust : 87% of consumers wouldn’t transact with a company if they had qualms with its security practices. 

  To improve your security posture, analyse where you are storing sensitive consumer data, who has access to it (internally and externally), and how you are protecting it. Then work with cybersecurity specialists on implementing stronger consumer security mechanisms (e.g. auto-log offs, secure password policy, etc) and extra internal security policies (if needed). 

  At the same time, start practising data minimisation. Ensure that all collected data is : 

  • Adequate – sufficient to meet your stated objectives 
  • Relevant – is rationally linked to the objectives 
  • Limited – no unnecessary data is collected or stored
  • Timely – data is periodically reviewed and removed when unnecessary 

  

  These principles prevent data hoarding. Also, they help improve your security posture and regulatory compliance by reducing the volume of information you need to safeguard.

  3. Do an Inventory of Your Business Tools

  Data leaks and consumer privacy breaches often occur through third parties. Because Google Analytics was deemed in breach of European GDPR in France, Austria and Italy, businesses using it are vulnerable to lawsuits (which are already happening). 

  Investigate your corporate toolkit to determine “weak links” – tools with controversial privacy policies, murky data collection practices, and poor security. 

  Treat it as a journey and pick your battles. By relying on Big Tech products for years, you might have overlooked better alternatives. 

  For example :

  • Matomo is a privacy-centred Google Analytics alternative. Our web analytics is compliant with GDPR, CCPA, and other global privacy laws. Unlike Google Analytics, we don’t exploit any data you collect and provide full transparency into how and where it’s stored. Or if you want a simple analytics solution, Fathom is another great privacy-friendly option.

  

  • For online data storage, you can choose Proton Drive or Nextcloud (open-source). Or host your corporate data with a local cloud hosting provider to avoid cross-border data transfers.

  

  Aim to progressively replace Google products with more privacy-centred alternatives. 

  4. Cultivate a Privacy-Centred Corporate Culture 

  To make privacy a competitive advantage, you need every team member (at every level) to respect its importance. 

  This is a continuous process of inspiring and educating your people. Find “privacy ambassadors” who are willing to lead the conversations, educate others, and provide resources for leading the change. 

  On an operational level, incorporate privacy principles around data minimisation, bounded collection, and usage into your Code of Conduct, standard operating procedures (SOPs), and other policies. 

  Creating a privacy-centric culture takes effort, but it pays off well. Cisco estimates that for each dollar spent on privacy, an average organisation gets $2.70 in associated benefits. Almost half (47%) of organisations gain 2X returns on their privacy initiatives.

  Moving Forward with a Data Privacy Programme 

  Privacy has become a strong differentiator for brands. Consumers crave transparency and ethical data usage. Regulators mandate limited data collection and proper security mechanisms.

  But sweeping changes are hard to implement. So start small and go one step at a time. Understand which first-party data your company collects and how it is stored.

  Then look into the tools and technologies you are using for data collection. Do these provide sufficient privacy controls ? How are they using data collected on your behalf ? Finally, move to wider transformations, pertaining to data management, cybersecurity, and cultural practices. 

  Be consistent with your effort — and eventually, all the pieces will fall into place.