Recherche avancée

Sur d’autres sites (12037)

• Error Writing trailer of rtmp FFMPEG Stream

  16 juin 2023, par Hellwraith

  I'm currently playing around with ffmpeg. And I put together the following command line. But for some time now I've been encountering the problem that ffmpeg keeps giving me with the error : Error writing trailer of rtmp :[Streamlink][Streamkey] : Broken Pipe

  


  aborts

  


  Does anyone know a workaround ?

  


  The command looks like this : ffmpeg -re -f concat -i Playlist.txt -codec copy -flvflags no_duration_filesize -f flv rtmp ://Streamlink/Streamkey

  


• Trying to fix VPlayer's seeking ability, need some guidance [Android FFmpeg]

  1er juin 2016, par vxh.viet

  I’m trying to fix the currently broken seeking ability of VPlayer which is a FFmpeg player for Android. Being a Java developer, C code looks like alien language to me so can only fix it using common logic (which could make any C veteran have a good laugh).

  The relevant file is player.c and I’ll try my best to point out the relevant modification.

  So the basic idea is because FFmpeg’s av_seek_frame is very inaccurate even with AVSEEK_FLAG_ANY so I’m trying to follow this suggestion to seek backward to the nearest keyframe and then decode to the frame I want. One addition note is since I want to seek based on millisecond while the said solution show the way to seek by frame which is potentially a source of problem.

  In the Player I add the following fields :

  struct Player{
  
  ....
  
  AVFrame *frame;
  
  int64_t current_time_stamp;
  
  }; 

  In the player_read_from_stream I modify the seeking part as :

  void * player_read_from_stream(void *data) {
  
      ...
  
      struct DecoderData *decoder_data = data;
  
      int stream_no = decoder_data->stream_no;
  
      AVCodecContext * ctx = player->input_codec_ctxs[stream_no];
  
      ...
  
      // seeking, start my stuff
  
      if(av_seek_frame(player->input_format_ctx, seek_input_stream_number, seek_target, AVSEEK_FLAG_BACKWARD) >= 0){
  
          //seek to key frame success, now need to read every frame from the key frame to our target time stamp
  
  
  
  
  
          while(player->current_time_stamp < seek_target){
  
  
  
              int frame_done;
  
  
  
              while (av_read_frame(player->input_format_ctx, &packet) >= 0) {
  
                  if (packet.stream_index == seek_input_stream_number) {
  
  
  
                      avcodec_decode_video2(ctx, player->frame, &frame_done, &packet);
  
                      LOGI(1,"testing_stuff ctx %d", *ctx);
  
                      if (frame_done) {
  
  
  
                          player->current_time_stamp = packet.dts;
  
                          LOGI(1,"testing_stuff current_time_stamp: %"PRId64, player->current_time_stamp);
  
                          av_free_packet(&packet);
  
                          return;
  
                      }
  
                  }
  
                  av_free_packet(&packet);
  
              }
  
          }
  
  
  
  
  
      }
  
      //end my stuff
  
  
  
      LOGI(3, "player_read_from_stream seeking success");
  
  
  
      int64_t current_time = av_gettime();
  
      player->start_time = current_time - player->seek_position;
  
      player->pause_time = current_time;        
  
  }

  And in player_alloc_frames I allocate the memory for my frame as :

  int player_alloc_frames(struct Player *player) {
  
      int capture_streams_no = player->caputre_streams_no;
  
      int stream_no;
  
      for (stream_no = 0; stream_no < capture_streams_no; ++stream_no) {
  
          player->input_frames[stream_no] = av_frame_alloc();
  
  
  
          //todo: test my stuff
  
          player->frame = av_frame_alloc();
  
          //end test
  
  
  
          if (player->input_frames[stream_no] == NULL) {
  
              return -ERROR_COULD_NOT_ALLOC_FRAME;
  
          }
  
      }
  
      return 0;
  
  }

  Currently it just keep crashing and being a typical Android NDK’s "feature", it just provide a super helpful stack trace :

  libc: Fatal signal 11 (SIGSEGV), code 1, fault addr 0x40 in tid 2717 (FFmpegReadFromS)

  I very much appreciate if anyone could help me solve this problem. Thank you for your time.

• Privacy-friendly analytics : The benefits of an ethical, GDPR-compliant platform

  13 juin, par Joe

  Your visitors shouldn’t feel like you’re spying on them — even if you’re just trying to improve the user experience or track your marketing efforts. 

  While many analytics platforms make customers feel that way thanks to intrusive cookie consent banners and highly personalised ads, there is a growing movement towards ethical, privacy-friendly analytics.

  In this article, you’ll learn what privacy-friendly analytics is, why it matters, what to look for in a solution and which of the leading providers is right for you. 

  What is privacy-friendly analytics ? 

  Privacy-friendly analytics is a form of website analytics that collects and analyses data in a way that respects the user’s privacy. It’s a type of ethical web analytics.

  Privacy-friendly platforms limit personal data collection and anonymise individual user data while being transparent about collection and tracking methods. They help companies adhere to data protection laws (like GDPR, CCPA, and HIPAA) and new privacy laws (like OCPA, FDBR, and TDPSA) without configuring custom settings. 

  Why use privacy-friendly analytics ? 

  Millions of businesses choose privacy-friendly analytics platforms like Matomo. Here are a few reasons why : 

  Build trust with customers

  Research shows that the vast majority of consumers don’t trust companies with their data, believing that they prioritise profits over data protection. 

  Privacy-friendly analytics can help businesses prove they aren’t out to profit from consumer data and regain customer trust. This can ultimately boost revenue. According to Cisco’s Data Privacy Benchmark Study, organisations gain $180 for every $100 spent on privacy. 

  Comply with privacy regulations

  Data privacy regulations, such as GDPR, protect consumer privacy and establish strict rules governing how businesses can collect and use personal data.

  The cost of non-compliance is high. Under GDPR, fines can be up to €20 million, or 4% of worldwide annual revenue.

  Thanks to features like data anonymisation and the default use of first-party cookies, privacy-friendly analytics platforms can support and strengthen compliance efforts. 

  In fact, the French Data Protection Authority (CNIL) approved Matomo as one of the only web analytics tools to collect data without tracking consent.

  Minimise the impact of a breach

  According to IBM’s Cost of a Data Breach report, the average cost of a data breach is nearly $4.5 million. The more personally identifiable information (PII) is involved, the higher the fines and penalties. 

  A privacy-friendly analytics tool can reduce the potential impact of a breach by minimising the amount of personal information you hold. 

  Is Google Analytics privacy-friendly ?

  Google may be the best-known analytics platform, but it’s not the best choice for businesses that want to collect data responsibly and ethically. 

  Here are just a few of Google Analytics’s privacy issues :

  • It uses analytics data to run its advertising business.
  • It may train large language models like Gemini with analytics data.
  • It requires a specific setup to be GDPR compliant that isn’t available out of the box.

  Google Analytics’s ongoing issues with privacy laws like GDPR also raise doubt. The French and Austrian Data Protection Authorities have banned Google Analytics in the past, and there is no guarantee they won’t do so again. 

  What to look for in privacy-friendly analytics ?

  Several privacy-friendly analytics tools are available. To find the right one for your brand, look for the following features.

  Data ownership

  Choose a provider that gives you as much control over your users’ data as possible. Ideally, this will be via an on-site solution where you store data on your servers. For cloud-based options, ensure your analytics provider can’t access, use or sell it.

  With 100% data ownership, you have the power to protect your users’ privacy. You know where your customer data is stored and what’s happening to it without external influence.

  Open source

  The only genuinely privacy-friendly software is open-source software. Open-source software means anyone can review the code to ensure it does what it promises — in this case, maximising privacy. 

  Matomo is an open-source software company. Our source code is on GitHub, where everyone can see precisely how our platform tracks and stores user data. A community of developers also regularly examines and reviews our code to further strengthen security. 

  Data anonymisation 

  Privacy-friendly analytics should allow marketers to completely anonymise the data they collect. They achieve this through several techniques like IP anonymisation and pseudonymised user IDs that modify or remove personally identifiable data so it can’t be linked to individuals.

  

  Matomo’s data anonymisation settings 

  In Matomo, for example, you can anonymise the following things in the platform’s Privacy settings :

  • IP address
  • Location
  • User ID

  IP address anonymisation is enabled by default in Matomo.

  No data sampling 

  Data sampling involves extrapolating analytics reports from an incomplete data set. Google Analytics uses this practice and relies on estimates, leading to incomplete and potentially inaccurate results.

  Privacy-friendly analytics should provide 100% accurate insights without making assumptions about your users’ data.

  GDPR compliance

  Privacy-friendly web analytics platforms adhere to even the strictest privacy laws, including GDPR, HIPAA and CCPA, thanks to the following features :

  • Data anonymisation
  • Cookieless tracking
  • EU data storage
  • First-party cookies by default

  

  Matomo data subject access request settings
  (Image Source)

  Privacy-first platforms also make it easy for companies to fulfil data subject access requests. In Matomo, for example, a dedicated feature lets you find, download and delete all of the data you hold about specific individuals. 

  Cookieless tracking

  Cookieless tracking is a form of visitor tracking that uses methods other than cookies to identify individual users. It is more privacy-friendly because no personal data is collected, and users can withhold consent from cookie banners.

  Matomo uses the most privacy-friendly industry-leading cookieless tracking method, config_id, to anonymously track visitors without fingerprinting them. 

  Top 3 privacy-friendly analytics platforms

  We’ve shortlisted three of the leading privacy-friendly analytics platforms. Learn what they offer, what makes them different and how much they cost.

  Matomo

  Matomo is an open-source web analytics tool and privacy-focused Google Analytics alternative trusted by over one million sites in over 190 countries and over 50 languages. 

  

  Matomo dashboard

  Matomo prioritises privacy and keeping businesses compliant with global privacy regulations like GDPR, CCPA and HIPAA. The data you collect is 100% accurate and yours alone. We don’t share it or use it for other purposes. 

  Benefits

  • Matomo’s all-in-one solution offers traditional web and behavioural analytics, such as heatmaps and session recordings. It also includes a free, open-source tag manager. 
  • Matomo gives you the choice of where to store your user’s data. With Matomo Cloud, that’s in our European servers. With Matomo On-Premise, that’s on your servers.
  • Matomo is open-source. Hundreds of independent developers have reviewed our code, and you can view it yourself on GitHub.

  Pricing 

  Hosting Matomo On-Premise is free, while Matomo Cloud costs $26 per month. 

  Fathom

  Fathom Analytics is a simple, easy-to-use alternative to Google Analytics that puts a premium on privacy. 

  

  Fathom dashboard
  (Image Source)

  Fathom has made its platform as easy to use as possible. You can install Fathom on any website or CMS using a single line of code. It also means the platform won’t massively impact your site’s speed or SEO performance. 

  Benefits

  • Fathom complies with all major privacy regulations, including GDPR and CCPA.
  • Fathom doesn’t sample data. It also blocks bots and scrapers, so you only see human visitors.
  • Fathom anonymises IP addresses, so you don’t have to show cookie banners.

  Drawbacks

  • Fathom doesn’t offer many of Matomo’s advanced features like e-commerce tracking, heatmaps, and session recordings.
  • The premium version of Fathom is not open-source. 

  Pricing 

  From $15 per month.

  Plausible

  Plausible Analytics is an open-source, privacy-friendly analytics tool built and hosted in the EU.

  

  Plausible dashboard
  (Image Source)

  The platform launched in 2019 as a lightweight, easy-to-use alternative to Google Analytics. Its simplicity is a big selling point. Instead of dozens of menus, it presents the information you need on a single page.

  Benefits

  • Plausible boasts an ultra-lightweight script, which means it has a minimal impact on page loading times. 
  • Plausible is GDPR and CCPA-compliant by design, so there’s no need for cookie banners.
  • Plausible is an open-source software with the source code available on GitHub.

  Drawbacks

  • Plausible lacks advanced privacy controls like a GDPR manager.
  • It has none of Matomo’s advanced features like A/B testing, session recordings or heatmaps. 

  Pricing 

  From $9 per month

  Try Matomo for free

  Ready to try a privacy-friendly analytics solution ? Making the switch is easy with Matomo, as it’s one of the only platforms to import historical Google Analytics data. You can also try Matomo for free for 21 days — no credit card required.