Recherche avancée

Sur d’autres sites (7258)

• Unable to merge videos in Android using JavaCV ("Sample Description" Error)

  15 décembre 2015, par San

  I am creating a video from images via FFMPEG and I am able to get the video from images. I am also making use of JavaCV to merge two videos and I am able to join videos using JavaCV without any issues provided both the videos are taken via camera, i.e, a video actually recorded via mobile camera.

  Issue that I’m facing :

  I am not able to merge the video that was generated from FFMPEG using the images along with the video user has chosen which will mostly be a video that was not generated and taken via mobile camera.

  CODE :
  Code to generate Video via Images :

                    FFmpegFrameRecorder recorder = new FFmpegFrameRecorder(path + "/" + "dec16.mp4", 800, 400);
  
                              try {
  
                                  recorder.setVideoCodec(avcodec.AV_CODEC_ID_MPEG4);
  
                                  //recorder.setVideoCodec(avcodec.AV_CODEC_ID_H264);
  
                                  recorder.setVideoCodecName("H264");
  
                                  recorder.setVideoOption("preset", "ultrafast");
  
                                  recorder.setFormat("mp4");
  
                                  recorder.setFrameRate(frameRate);
  
                                  recorder.setVideoBitrate(60);
  
                                  recorder.setPixelFormat(avutil.AV_PIX_FMT_YUV420P);
  
                                  startTime = System.currentTimeMillis();
  
                                  recorder.start();
  
                                  for(int j=0;j recorder.getTimestamp()) {
  
  
  
                                          recorder.setTimestamp(t);
  
                                          recorder.record(image);
  
                                      }
  
                                  }
  
                                  recorder.stop();
  
                              } catch (Exception e) {
  
                                  e.printStackTrace();
  
                              }

  Code to merge Videos :

   int count = file_path.size();
  
              System.out.println("final_joined_list size " + file_path.size());
  
              if (file_path.size() != 1) {
  
                  try {
  
                      Movie[] inMovies = new Movie[count];
  
                      mediaStorageDir = new File(
  
                              Environment.getExternalStorageDirectory()
  
                                      + "/Pictures");
  
  
  
                      for (int i = 0; i < count; i++) {
  
                          File file = new File(file_path.get(i));
  
                          System.out.println("fileeeeeeeeeeeeeeee " + file);
  
                          System.out.println("file exists!!!!!!!!!!");
  
  
  
                          FileInputStream fis = new FileInputStream(file);
  
                          FileChannel fc = fis.getChannel();
  
                          inMovies[i] = MovieCreator.build(fc);
  
                          fis.close();
  
                          fc.close();
  
  
  
                      }
  
                      List<track> videoTracks = new LinkedList<track>();
  
                      List<track> audioTracks = new LinkedList<track>();
  
                      Log.d("Movies length", "isss  " + inMovies.length);
  
                      if (inMovies.length != 0) {
  
  
  
                          for (Movie m : inMovies) {
  
  
  
                              for (Track t : m.getTracks()) {
  
                                  if (t.getHandler().equals("soun")) {
  
                                      audioTracks.add(t);
  
                                  }
  
                                  if (t.getHandler().equals("vide")) {
  
                                      videoTracks.add(t);
  
                                  }
  
                                  if (t.getHandler().equals("")) {
  
  
  
                                  }
  
                              }
  
  
  
                          }
  
                      }
  
  
  
                      Movie result = new Movie();
  
  
  
                      System.out.println("audio and videoo tracks : "
  
                              + audioTracks.size() + " , " + videoTracks.size());
  
                      if (audioTracks.size() > 0) {
  
                          result.addTrack(new AppendTrack(audioTracks
  
                                  .toArray(new Track[audioTracks.size()])));
  
                      }
  
                      if (videoTracks.size() > 0) {
  
                          result.addTrack(new AppendTrack(videoTracks
  
                                  .toArray(new Track[videoTracks.size()])));
  
                      }
  
                      IsoFile out = null;
  
                      try {
  
                          out = (IsoFile) new DefaultMp4Builder().build(result);
  
                      } catch (Exception e) {
  
                          // TODO Auto-generated catch block
  
                          e.printStackTrace();
  
                      }
  
  
  
                      long timestamp = new Date().getTime();
  
                      String timestampS = "" + timestamp;
  
  
  
                      File storagePath = new File(mediaStorageDir
  
                              + File.separator);
  
                      storagePath.mkdirs();
  
                      File myMovie = new File(storagePath, String.format("%s.mp4", timestampS));
  
                      FileOutputStream fos = new FileOutputStream(myMovie);
  
                      FileChannel fco = fos.getChannel();
  
                      fco.position(0);
  
                      out.getBox(fco);
  
                      fco.close();
  
                      fos.close();
  
  
  
                  } catch (FileNotFoundException e) {
  
                      // TODO Auto-generated catch block
  
                      e.printStackTrace();
  
                  } catch (IOException e) {
  
                      // TODO Auto-generated catch block
  
                      e.printStackTrace();
  
                  }
  
                  String mFileName = Environment.getExternalStorageDirectory()
  
                          .getAbsolutePath();
  
                  // mFileName += "/output.mp4";
  
  
  
                  File sdCardRoot = Environment.getExternalStorageDirectory();
  
                  File yourDir = new File(mediaStorageDir + File.separator);
  
                  for (File f : yourDir.listFiles()) {
  
                      if (f.isFile())
  
                          name = f.getName();
  
                      // make something with the name
  
                  }
  
                  mFileName = mediaStorageDir.getPath() + File.separator
  
                          + "output-%s.mp4";
  
                  System.out.println("final filename : "
  
                          + mediaStorageDir.getPath() + File.separator
  
                          + "output-%s.mp4" + "names of files : " + name);
  
                  single_video = false;
  
                  return name;
  
              } else {
  
                  single_video = true;
  
                  name = file_path.get(0);
  
                  return name;
  
              }
  
  </track></track></track></track>

  Error :

  The Error that I am facing while trying to merge the videos generated via Images and a normal video is

  12-15 12:26:06.155  26022-26111/? W/System.err﹕ java.io.IOException: Cannot append com.googlecode.mp4parser.authoring.Mp4TrackImpl@45417c38 to com.googlecode.mp4parser.authoring.Mp4TrackImpl@44ffac60 since their Sample Description Boxes differ
  
  12-15 12:26:06.155  26022-26111/? W/System.err﹕ at com.googlecode.mp4parser.authoring.tracks.AppendTrack.<init>(AppendTrack.java:48)
  
  </init>

  Fix that I tried :

  Google advised me to change the CODEC in JavaCV from avcodec.AV_CODEC_ID_MPEG4 to avcodec.AV_CODEC_ID_H264. But when I did that, I am not able to get the video from images thereby throwing the following error :

  12-15 12:26:05.840  26022-26089/? W/linker﹕ libavcodec.so has text relocations. This is wasting memory and is a security risk. Please fix.
  
  12-15 12:26:05.975  26022-26089/? W/System.err﹕ com.googlecode.javacv.FrameRecorder$Exception: avcodec_open2() error -1: Could not open video codec.
  
  12-15 12:26:05.975  26022-26089/? W/System.err﹕ at com.googlecode.javacv.FFmpegFrameRecorder.startUnsafe(FFmpegFrameRecorder.java:492)
  
  12-15 12:26:05.975  26022-26089/? W/System.err﹕ at com.googlecode.javacv.FFmpegFrameRecorder.start(FFmpegFrameRecorder.java:267)

  What I need :

  Creating video from Images is inevitable and that video will definitely be used to merge with other videos which might have any Codec Formats. So I need to find a way to merge any kind of videos irrespective of their Codecs or any other parameters. I am trying to keep it simple by just using the Jars and SO files and I dont want to drive myself crazy by going on a full scale implementation of FFMPEG Library. That being said, I am also ready to look into that library if I dont have any other ways to achieve what I want but a solid resource with an ALMOST working code would be much appreciated. Cheers.

  Update :
  I looked upon the issues mentioned at GitHub of OpenCV, but didnt find anything solid from it.
  OpenCV Issues

• How to Choose the Optimal Multi-Touch Attribution Model for Your Organisation

  13 mars 2023, par Erin — Analytics Tips

  If you struggle to connect the dots on your customer journeys, you are researching the correct solution. 

  Multi-channel attribution models allow you to better understand the users’ paths to conversion and identify key channels and marketing assets that assist them.

  That said, each attribution model has inherent limitations, which make the selection process even harder.

  This guide explains how to choose the optimal multi-touch attribution model. We cover the pros and cons of popular attribution models, main evaluation criteria and how-to instructions for model implementation. 

  Pros and Cons of Different Attribution Models 

  

  First Interaction 

  First Interaction attribution model (also known as first touch) assigns full credit to the conversion to the first channel, which brought in a lead. However, it doesn’t report other interactions the visitor had before converting.

  Marketers, who are primarily focused on demand generation and user acquisition, find the first touch attribution model useful to evaluate and optimise top-of-the-funnel (ToFU). 

  Pros 

  • Reflects the start of the customer journey
  • Shows channels that bring in the best-qualified leads 
  • Helps track brand awareness campaigns

  Cons 

  • Ignores the impact of later interactions at the middle and bottom of the funnel 
  • Doesn’t provide a full picture of users’ decision-making process 

  Last Interaction 

  Last Interaction attribution model (also known as last touch) shifts the entire credit allocation to the last channel before conversion. But it doesn’t account for the contribution of all other channels. 

  If your focus is conversion optimization, the last-touch model helps you determine which channels, assets or campaigns seal the deal for the prospect. 

  Pros 

  • Reports bottom-of-the-funnel events
  • Requires minimal data and configurations 
  • Helps estimate cost-per-lead or cost-per-acquisition

  Cons 

  • No visibility into assisted conversions and prior visitor interactions 
  • Overemphasise the importance of the last channel (which can often be direct traffic) 

  Last Non-Direct Interaction 

  Last Non-Direct attribution excludes direct traffic from the calculation and assigns the full conversion credit to the preceding channel. For example, a paid ad will receive 100% of credit for conversion if a visitor goes directly to your website to buy a product. 

  Last Non-Direct attribution provides greater clarity into the bottom-of-the-funnel (BoFU). events. Yet, it still under-reports the role other channels played in conversion. 

  Pros 

  • Improved channel visibility, compared to Last-Touch 
  • Avoids over-valuing direct visits
  • Reports on lead-generation efforts

  Cons 

  • Doesn’t work for account-based marketing (ABM) 
  • Devalues the quality over quantity of leads 

  Linear Model

  Linear attribution model assigns equal credit for a conversion to all tracked touchpoints, regardless of their impact on the visitor’s decision to convert.

  It helps you understand the full conversion path. But this model doesn’t distinguish between the importance of lead generation activities versus nurturing touches.

  Pros 

  • Focuses on all touch points associated with a conversion 
  • Reflects more steps in the customer journey 
  • Helps analyse longer sales cycles

  Cons 

  • Doesn’t accurately reflect the varying roles of each touchpoint 
  • Can dilute the credit if too many touchpoints are involved 

  Time Decay Model 

  Time decay models assumes that the closer a touchpoint is to the conversion, the greater its influence. Pre-conversion touchpoints get the highest credit, while the first ones are ranked lower (5%-5%-10%-15%-25%-30%).

  This model better reflects real-life customer journeys. However, it devalues the impact of brand awareness and demand-generation campaigns. 

  Pros 

  • Helps track longer sales cycles and reports on each touchpoint involved 
  • Allows customising the half-life of decay to improve reporting 
  • Promotes conversion optimization at BoFu stages

  Cons 

  • Can prompt marketers to curtail ToFU spending, which would translate to fewer qualified leads at lower stages
  • Doesn’t reflect highly-influential events at earlier stages (e.g., a product demo request or free account registration, which didn’t immediately lead to conversion)

  Position-Based Model 

  Position-Based attribution model (also known as the U-shaped model) allocates the biggest credit to the first and the last interaction (40% each). Then distributes the remaining 20% across other touches. 

  For many marketers, that’s the preferred multi-touch attribution model as it allows optimising both ToFU and BoFU channels. 

  Pros 

  • Helps establish the main channels for lead generation and conversion
  • Adds extra layers of visibility, compared to first- and last-touch attribution models 
  • Promotes budget allocation toward the most strategic touchpoints

  Cons 

  • Diminishes the importance of lead nurturing activities as more credit gets assigned to demand-gen and conversion-generation channels
  • Limited flexibility since it always assigns a fixed amount of credit to the first and last touchpoints, and the remaining credit is divided evenly among the other touchpoints

  How to Choose the Right Multi-Touch Attribution Model For Your Business 

  If you’re deciding which attribution model is best for your business, prepare for a heated discussion. Each one has its trade-offs as it emphasises or devalues the role of different channels and marketing activities.

  To reach a consensus, the best strategy is to evaluate each model against three criteria : Your marketing objectives, sales cycle length and data availability. 

  Marketing Objectives 

  Businesses generate revenue in many ways : Through direct sales, subscriptions, referral fees, licensing agreements, one-off or retainer services. Or any combination of these activities. 

  In each case, your marketing strategy will look different. For example, SaaS and direct-to-consumer (DTC) eCommerce brands have to maximise both demand generation and conversion rates. In contrast, a B2B cybersecurity consulting firm is more interested in attracting qualified leads (as opposed to any type of traffic) and progressively nurturing them towards a big-ticket purchase. 

  When selecting a multi-touch attribution model, prioritise your objectives first. Create a simple scoreboard, where your team ranks various channels and campaign types you rely on to close sales. 

  Alternatively, you can survey your customers to learn how they first heard about your company and what eventually triggered their conversion. Having data from both sides can help you cross-validate your assumptions and eliminate some biases. 

  Then consider which model would best reflect the role and importance of different channels in your sales cycle. Speaking of which….

  Sales Cycle Length 

  As shoppers, we spend less time deciding on a new toothpaste brand versus contemplating a new IT system purchase. Factors like industry, business model (B2C, DTC, B2B, B2BC), and deal size determine the average cycle length in your industry. 

  Statistically, low-ticket B2C sales can happen within just several interactions. The average B2B decision-making process can have over 15 steps, spread over several months. 

  That’s why not all multi-touch attribution models work equally well for each business. Time-decay suits better B2B companies, while B2C usually go for position-based or linear attribution. 

  Data Availability 

  Businesses struggle with multi-touch attribution model implementation due to incomplete analytics data. 

  Our web analytics tool captures more data than Google Analytics. That’s because we rely on a privacy-focused tracking mechanism, which allows you to collect analytics without showing a cookie consent banner in markets outside of Germany and the UK. 

  Cookie consent banners are mandatory with Google Analytics. Yet, almost 40% of global consumers reject it. This results in gaps in your analytics and subsequent inconsistencies in multi-touch attribution reports. With Matomo, you can compliantly collect more data for accurate reporting. 

  Some companies also struggle to connect collected insights to individual shoppers. With Matomo, you can cross-attribute users across browning sessions, using our visitors’ tracking feature. 

  When you already know a user’s identifier (e.g., full name or email address), you can track their on-site behaviours over time to better understand how they interact with your content and complete their purchases. Quick disclaimer, though, visitors’ tracking may not be considered compliant with certain data privacy laws. Please consult with a local authority if you have doubts. 

  How to Implement Multi-Touch Attribution

  Multi-touch attribution modelling implementation is like a “seek and find” game. You have to identify all significant touchpoints in your customers’ journeys. And sometimes also brainstorm new ways to uncover the missing parts. Then figure out the best way to track users’ actions at those stages (aka do conversion and events tracking). 

  Here’s a step-by-step walkthrough to help you get started. 

  Select a Multi-Touch Attribution Tool 

  The global marketing attribution software is worth $3.1 billion. Meaning there are plenty of tools, differing in terms of accuracy, sophistication and price.

  To make the right call prioritise five factors :

  • Available models : Look for a solution that offers multiple options and allows you to experiment with different modelling techniques or develop custom models. 
  • Implementation complexity : Some providers offer advanced data modelling tools for creating custom multi-touch attribution models, but offer few out-of-the-box modelling options. 
  • Accuracy : Check if the shortlisted tool collects the type of data you need. Prioritise providers who are less dependent on third-party cookies and allow you to identify repeat users. 
  • Your marketing stack : Some marketing attribution tools come with useful add-ons such as tag manager, heatmaps, form analytics, user session recordings and A/B testing tools. This means you can collect more data for multi-channel modelling with them instead of investing in extra software. 
  • Compliance : Ensure that the selected multi-attribution analytics software wouldn’t put you at risk of GDPR non-compliance when it comes to user privacy and consent to tracking/analysis. 

  Finally, evaluate the adoption costs. Free multi-channel analytics tools come with data quality and consistency trade-offs. Premium attribution tools may have “hidden” licensing costs and bill you for extra data integrations. 

  Look for a tool that offers a good price-to-value ratio (i.e., one that offers extra perks for a transparent price). 

  Set Up Proper Data Collection 

  Multi-touch attribution requires ample user data. To collect the right type of insights you need to set up : 

  • Website analytics : Ensure that you have all tracking codes installed (and working correctly !) to capture pageviews, on-site actions, referral sources and other data points around what users do on page. 
  • Tags : Add tracking parameters to monitor different referral channels (e.g., “facebook”), campaign types (e.g., ”final-sale”), and creative assets (e.g., “banner-1”). Tags help you get a clearer picture of different touchpoints. 
  • Integrations : To better identify on-site users and track their actions, you can also populate your attribution tool with data from your other tools – CRM system, A/B testing app, etc. 

  Finally, think about the ideal lookback window — a bounded time frame you’ll use to calculate conversions. For example, Matomo has a default windows of 7, 30 or 90 days. But you can configure a custom period to better reflect your average sales cycle. For instance, if you’re selling makeup, a shorter window could yield better results. But if you’re selling CRM software for the manufacturing industry, consider extending it.

  Configure Goals and Events 

  Goals indicate your main marketing objectives — more traffic, conversions and sales. In web analytics tools, you can measure these by tracking specific user behaviours. 

  For example : If your goal is lead generation, you can track :

  • Newsletter sign ups 
  • Product demo requests 
  • Gated content downloads 
  • Free trial account registration 
  • Contact form submission 
  • On-site call bookings 

  In each case, you can set up a unique tag to monitor these types of requests. Then analyse conversion rates — the percentage of users who have successfully completed the action. 

  To collect sufficient data for multi-channel attribution modelling, set up Goal Tracking for different types of touchpoints (MoFU & BoFU) and asset types (contact forms, downloadable assets, etc). 

  Your next task is to figure out how users interact with different on-site assets. That’s when Event Tracking comes in handy. 

  Event Tracking reports notify you about specific actions users take on your website. With Matomo Event Tracking, you can monitor where people click on your website, on which pages they click newsletter subscription links, or when they try to interact with static content elements (e.g., a non-clickable banner). 

  Using in-depth user behavioural reports, you can better understand which assets play a key role in the average customer journey. Using this data, you can localise “leaks” in your sales funnel and fix them to increase conversion rates.

  Test and Validated the Selected Model 

  A common challenge of multi-channel attribution modelling is determining the correct correlation and causality between exposure to touchpoints and purchases. 

  For example, a user who bought a discounted product from a Facebook ad would act differently than someone who purchased a full-priced product via a newsletter link. Their rate of pre- and post-sales exposure will also differ a lot — and your attribution model may not always accurately capture that. 

  That’s why you have to continuously test and tweak the selected model type. The best approach for that is lift analysis. 

  Lift analysis means comparing how your key metrics (e.g., revenue or conversion rates) change among users who were exposed to a certain campaign versus a control group. 

  In the case of multi-touch attribution modelling, you have to monitor how your metrics change after you’ve acted on the model recommendations (e.g., invested more in a well-performing referral channel or tried a new brand awareness Twitter ad). Compare the before and after ROI. If you see a positive dynamic, your model works great. 

  The downside of this approach is that you have to invest a lot upfront. But if your goal is to create a trustworthy attribution model, the best way to validate is to act on its suggestions and then test them against past results. 

  Conclusion

  A multi-touch attribution model helps you measure the impact of different channels, campaign types, and marketing assets on metrics that matter — conversion rate, sales volumes and ROI. 

  Using this data, you can invest budgets into the best-performing channels and confidently experiment with new campaign types. 

  As a Matomo user, you also get to do so without breaching customers’ privacy or compromising on analytics accuracy.

  Start using accurate multi-channel attribution in Matomo. Get your free 21-day trial now. No credit card required.

• AAC encoder : Extensive improvements

  11 octobre 2015, par Claudio Freire

  AAC encoder : Extensive improvements
  This finalizes merging of the work in the patches in ticket #2686.
  Improvements to twoloop and RC logic are extensive.
  The non-exhaustive list of twoloop improvments includes :
  
   - Tweaks to distortion limits on the RD optimization phase of twoloop
  
   - Deeper search in twoloop
  
   - PNS information marking to let twoloop decide when to use it
  
     (turned out having the decision made separately wasn’t working)
  
   - Tonal band detection and priorization
  
   - Better band energy conservation rules
  
   - Strict hole avoidance
  For rate control :
  
   - Use psymodel’s bit allocation to allow proper use of the bit
  
     reservoir. Don’t work against the bit reservoir by moving lambda
  
     in the opposite direction when psymodel decides to allocate more/less
  
     bits to a frame.
  
   - Retry the encode if the effective rate lies outside a reasonable
  
     margin of psymodel’s allocation or the selected ABR.
  
   - Log average lambda at the end. Useful info for everyone, but especially
  
     for tuning of the various encoder constants that relate to lambda
  
     feedback.
  Psy :
  
   - Do not apply lowpass with a FIR filter, instead just let the coder
  
     zero bands above the cutoff. The FIR filter induces group delay,
  
     and while zeroing bands causes ripple, it’s lost in the quantization
  
     noise.
  
   - Experimental VBR bit allocation code
  
   - Tweak automatic lowpass filter threshold to maximize audio bandwidth
  
     at all bitrates while still providing acceptable, stable quality.
  I/S :
  
   - Phase decision fixes. Unrelated to #2686, but the bugs only surfaced
  
     when the merge was finalized. Measure I/S band energy accounting for
  
     phase, and prevent I/S and M/S from being applied both.
  PNS :
  
   - Avoid marking short bands with PNS when they’re part of a window
  
     group in which there’s a large variation of energy from one window
  
     to the next. PNS can’t preserve those and the effect is extremely
  
     noticeable.
  M/S :
  
   - Implement BMLD protection similar to the specified in
  
     ISO-IEC/13818:7-2003, Appendix C Section 6.1. Since M/S decision
  
     doesn’t conform to section 6.1, a different method had to be
  
     implemented, but should provide equivalent protection.
  
   - Move the decision logic closer to the method specified in
  
     ISO-IEC/13818:7-2003, Appendix C Section 6.1. Specifically,
  
     make sure M/S needs less bits than dual stereo.
  
   - Don’t apply M/S in bands that are using I/S
  Now, this of course needed adjustments in the compare targets and
  
  fuzz factors of the AAC encoder’s fate tests, but if wondering why
  
  the targets go up (more distortion), consider the previous coder
  
  was using too many bits on LF content (far more than required by
  
  psy), and thus those signals will now be more distorted, not less.
  The extra distortion isn’t audible though, I carried extensive
  
  ABX testing to make sure.
  A very similar patch was also extensively tested by Kamendo2 in
  
  the context of #2686.
  

  • [DH] Changelog
  • [DH] libavcodec/aac.h
  • [DH] libavcodec/aaccoder.c
  • [DH] libavcodec/aaccoder_trellis.h
  • [DH] libavcodec/aaccoder_twoloop.h
  • [DH] libavcodec/aacenc.c
  • [DH] libavcodec/aacenc.h
  • [DH] libavcodec/aacenc_is.c
  • [DH] libavcodec/aacenc_is.h
  • [DH] libavcodec/aacenc_pred.c
  • [DH] libavcodec/aacenc_quantization.h
  • [DH] libavcodec/aacenc_utils.h
  • [DH] libavcodec/aacpsy.c
  • [DH] libavcodec/mathops.h
  • [DH] libavcodec/mips/aaccoder_mips.c
  • [DH] libavcodec/psymodel.c
  • [DH] libavcodec/psymodel.h
  • [DH] tests/fate/aac.mak