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• Issues with video frame dropout using Accord.NET VideoFileWriter and FFMPEG

  9 janvier 2018, par David

  I am testing out writing video files using the Accord.Video library. I have a WPF project created in Visual Studio 2017, and I have installed Accord.Video.FFMPEG as well as Accord.Video.VFW using Nuget, as well as their dependencies.

  I have created a very simple video to test basic file output. However, I am running into some issues. My goal is to be able to output videos with a variable frame rate, because in the future I will be using this code to input images from a webcam device that will then be saved to a video file, and video from webcams typically has variable frame rates.

  For now, in this example, I am not inputting video from a webcam, but rather I am generating a simple "moving box" image and outputting the frames to a video file. The box changes color every 20 frames : red, green, blue, yellow, and finally white. I also set the frame rate to be 20 fps.

  When I use Accord.Video.VFW, the frame rate is correctly set, and all the frames are correctly outputted to the video file. The resulting video looks like this (see the YouTube link) : https://youtu.be/K8E9O7bJIbg

  This is just a reference, however. I don’t intend on using Accord.Video.VFW because it outputs uncompressed data to an AVI file, and it doesn’t support variable frame rates. I would like to use Accord.Video.FFMPEG because it is supposed to support variable frame rates.

  When I attempt to use the Accord.Video.FFMPEG library, however, the video does not result in how I would expect it to look. Here is a YouTube link : https://youtu.be/cW19yQFUsLI

  As you can see, in that example, the box remains the first color for a longer amount of time than the other colors. It also never reaches the final color (white). When I inspect the video file, 100 frames were not outputted to the file. There are 69 or 73 frames typically. And the expected frame rate and duration obviously do not match up.

  Here is the code that generates both these videos :

  public MainWindow()
  
  {
  
      InitializeComponent();
  
  
  
      Accord.Video.VFW.AVIWriter avi_writer = new Accord.Video.VFW.AVIWriter();
  
      avi_writer.FrameRate = 20;
  
      avi_writer.Open("test2.avi", 640, 480);
  
  
  
      Accord.Video.FFMPEG.VideoFileWriter k = new Accord.Video.FFMPEG.VideoFileWriter();
  
      k.FrameRate = 20;
  
      k.Width = 640;
  
      k.Height = 480;
  
      k.Open("test.mp4");
  
      for (int i = 0; i < 100; i++)
  
      {
  
          TimeSpan t = new TimeSpan(0, 0, 0, 0, 50 * i);
  
          var b = new System.Drawing.Bitmap(640, 480);
  
          var g = Graphics.FromImage(b);
  
          var br = System.Drawing.Brushes.Blue;
  
          if (t.TotalMilliseconds < 1000)
  
              br = System.Drawing.Brushes.Red;
  
          else if (t.TotalMilliseconds < 2000)
  
              br = System.Drawing.Brushes.Green;
  
          else if (t.TotalMilliseconds < 3000)
  
              br = System.Drawing.Brushes.Blue;
  
          else if (t.TotalMilliseconds < 4000)
  
              br = System.Drawing.Brushes.Yellow;
  
          else
  
              br = System.Drawing.Brushes.White;
  
  
  
          g.FillRectangle(br, 50 + i, 50, 100, 100);
  
          System.Console.WriteLine("Frame: " + (i + 1).ToString() + ", Millis: " + t.TotalMilliseconds.ToString());
  
  
  
          #region This is the code in question
  
  
  
          k.WriteVideoFrame(b, t);
  
          avi_writer.AddFrame(b);
  
  
  
          #endregion
  
      }
  
  
  
      avi_writer.Close();
  
      k.Close();
  
      System.Console.WriteLine("Finished writing video");
  
  }

  I have tried changing a few things under the assumption that maybe the "WriteVideoFrame" function isn’t able to finish in time, and so I need to slow down the program so it can complete itself. Under that assumption, I have replaced the "WriteVideoFrame" call with the following code :

  Task taskA = new Task(() => k.WriteVideoFrame(b, t));
  
  taskA.Start();
  
  taskA.Wait();

  And I have tried the following code :

  Task.WaitAll(
  
      Task.Run( () =>
  
      { 
  
          lock(syncObj)
  
          {
  
              k.WriteVideoFrame(b, t);
  
          }
  
      }
  
  ));

  And even just a standard call where I don’t specify a timestamp :

  k.WriteVideoFrame(b);

  None of these work. They all result in something similar.

  Any suggestions on getting the WriteVideoFrame function to work that is a part of the Accord.Video.FFMPEG.VideoFileWriter class ?

  Thanks for any and all help !

  [edits below]

  I have done some more investigating. I still haven’t found a good solution, but here is what I have found so far. After declaring my VideoFileWriter object, I have tried setting up some options for the video.

  When I use an H264 codec with the following options, it correctly saves 100 frames at a frame-rate of 20 fps, however any normal media player (both VLC and Windows Media Player) end up playing a 10-second video instead of a 5-second video. Essentially, it seems like they play it at half-speed. Here is the code that gives that result :

  k.VideoCodec = Accord.Video.FFMPEG.VideoCodec.H264;
  
  k.VideoOptions["crf"] = "18";
  
  k.VideoOptions["preset"] = "veryfast";
  
  k.VideoOptions["tune"] = "zerolatency";
  
  k.VideoOptions["x264opts"] = "no-mbtree:sliced-threads:sync-lookahead=0";

  Additionally, if I use an Mpeg4 codec, I get the same "half-speed" result :

  k.VideoCodec = Accord.Video.FFMPEG.VideoCodec.Mpeg4;

  However, if I use a WMV codec, then it correctly results in 100 frames at 20 fps, and a 5 second video that is correctly played by both media players :

  k.VideoCodec = Accord.Video.FFMPEG.VideoCodec.Wmv1;

  Although this is good news, this still doesn’t solve the problem because WMV doesn’t support variable frame rates. Also, this still doesn’t answer the question as to why the problem is happening in the first place.

  As always, any help would be appreciated !

• Introducing Crash Analytics for Matomo

  30 août 2023, par Erin — Community, Plugins

  Bugs and development go hand in hand. As code matures, it contends with new browser iterations, clashes with ad blockers and other software quirks, resulting in the inevitable emergence of bugs. In fact, a staggering 13% of all pageviews come with lurking JavaScript errors.

  Monitoring for crashes becomes an unrelenting task. Amidst this never-ending effort to remove bugs, a SurveyMonkey study unveils a shared reality : a resounding 66% of individuals have encountered bug-ridden websites.

  These bugs lead to problems like malfunctioning shopping carts, glitchy checkout procedures and contact forms that just won’t cooperate. But they’re not just minor annoyances – they pose a real danger to your conversion rates and revenue.

  According to a study, 58% of visitors are inclined to abandon purchases as a result of bugs, while an astonishing 75% are driven to completely abandon websites due to these frustrating experiences.

  Imagine a website earning approximately 25,000 EUR per month. Now, factor in errors occurring in 13% of all pageviews. The result ? A potential monthly loss of 1,885 EUR.

  Meet Crash Analytics

  Driven by our vision to create an empowering analytics product, we’re excited to introduce Crash Analytics, an innovative plugin for Matomo On-Premise that automatically tracks bugs on your website.

  

  By offering insights into the precise bug location and the user’s interactions that triggered it, along with details about their device type, browser and more, Crash Analytics empowers you to swiftly address crashes, leading to an improved user experience, higher conversion rates and revenue growth.

  Soon, Crash Analytics will become available to Matomo Cloud users as well, so stay tuned for further updates and announcements.

  Say goodbye to lost revenue – never miss a bug again

  Even if you put your website through the toughest tests, it’s hard to predict every little hiccup that can pop up across different browsers, setups and situations. Factors such as ad blockers, varying internet speeds for visitors and browser updates can add an extra layer of complexity.

  When these crashes happen, you want to know immediately. However, according to a study, only 29% of surveyed respondents would report the existence of the site bug to the website operator. These bugs that go unnoticed can really hurt your bottom line and conversion rates, causing you to lose out on revenue and leaving your users frustrated and disappointed.

  

  Crash Analytics is here to bridge this gap. Armed with scheduled reporting (via email or texts) and automated alert functionalities, you gain the power to instantly detect bugs as they occur on your site. This proactive approach ensures that even the subtlest of issues are brought to your attention promptly. 

  With automated reports and alerts, you can also opt to receive notifications when crashes increase or ignore specific crashes that you deem insignificant. This keeps you in the loop with only the issues that truly matter, helping you cut out the noise and take immediate action.

  

  Easily forward crash data to developers and synchronise the efforts of technical teams and marketing experts. Track emerging, disappearing and recurring errors, ensuring that crash data is efficiently relayed to developers to prioritise fixes that matter.

  

  Plus, your finger is always on the pulse with real-time reports that offer a live view of crashes happening at the moment, an especially helpful feature after deploying changes. Use annotations to mark deploys and correlate them with crash data, enabling you to quickly identify if a new bug is linked to recent updates or modifications.

  

  And with our mobile app, you can effortlessly stay connected to your website’s performance, conveniently accessing crash information anytime and anywhere. This ensures you’re in complete control of your site’s health, even when you’re on the move.

  Streamline bug resolution with combined web and crash analytics

  Crash Analytics for Matomo doesn’t just stop at pinpointing bug locations ; it goes a step further by providing you with a holistic perspective of user interactions. Seamlessly combining Matomo’s traditional and behavioural web analytics features—like segments, session recordings and visitor logs—with crash data, this integrated approach unveils a wealth of insights so you can quickly resolve bugs. 

  For instance, let’s say a user encounters a bug while attempting to complete a purchase on your e-commerce website. Crash Analytics reveals the exact point of failure, but to truly grasp the situation, you delve into the session recordings. These recordings offer a front-row seat to the user’s journey—every click and interaction that led to the bug. Session recordings are especially helpful when you are struggling to reproduce an issue.

  

  Additionally, the combination of visitor logs with crash data offers a comprehensive timeline of a user’s engagement. This helps you understand their activity leading up to the bug, such as pages visited, actions taken and devices used. Armed with these multifaceted insights, you can confidently pinpoint the root causes and address the crash immediately.

  With segments, you have the ability to dissect the data and compare experiences among distinct user groups. For example, you can compare mobile visitors to desktop visitors to determine if the issue is isolated or widespread and what impact the issue is having on the user experience of different user groups. 

  The combination of crash data with Matomo’s comprehensive web analytics equips you with the tools needed to elevate user experiences and ultimately drive revenue growth.

  Start in seconds, shape as needed : Your path to a 100% reliable website

  Crash Analytics makes the path to a reliable website simple. You don’t have to deal with intricate setups—crash detection starts without any configuration. 

  Plus, Crash Analytics excels in cross-stack proficiency, seamlessly extending its capabilities beyond automatically tracking JavaScript errors to covering server-side crashes as well, whether they occur in PHP, Android, iOS, Java or other frameworks. This versatile approach ensures that Crash Analytics comprehensively supports your website’s health and performance across various technological landscapes.

  Elevate your website with Crash Analytics

  Experience the seamless convergence of bug tracking and web analytics, allowing you to delve into user interactions, session recordings and visitor logs. With the flexibility of customising real-time alerts and scheduled reports, alongside cross-stack proficiency, Crash Analytics becomes your trusted ally in enhancing your website’s reliability and user satisfaction to increase conversions and drive revenue growth. Equip yourself to swiftly address issues and create a website where user experiences take precedence.

  Start your 30-day free trial of our Crash Analytics plugin today, and stay tuned for its availability on Matomo Cloud.

• Combine Audio and Images in Stream

  19 décembre 2017, par SenorContento

  I would like to be able to create images on the fly and also create audio on the fly too and be able to combine them together into an rtmp stream (for Twitch or YouTube). The goal is to accomplish this in Python 3 as that is the language my bot is written in. Bonus points for not having to save to disk.

  So far, I have figured out how to stream to rtmp servers using ffmpeg by loading a PNG image and playing it on loop as well as loading a mp3 and then combining them together in the stream. The problem is I have to load at least one of them from file.

  I know I can use Moviepy to create videos, but I cannot figure out whether or not I can stream the video from Moviepy to ffmpeg or directly to rtmp. I think that I have to generate a lot of really short clips and send them, but I want to know if there’s an existing solution.

  There’s also OpenCV which I hear can stream to rtmp, but cannot handle audio.

  A redacted version of an ffmpeg command I have successfully tested with is

  ffmpeg -loop 1 -framerate 15 -i ScreenRover.png -i "Song-Stereo.mp3" -c:v libx264 -preset fast -pix_fmt yuv420p -threads 0 -f flv rtmp://SITE-SUCH-AS-TWITCH/.../STREAM-KEY

  or

  cat Song-Stereo.mp3 | ffmpeg -loop 1 -framerate 15 -i ScreenRover.png -i - -c:v libx264 -preset fast -pix_fmt yuv420p -threads 0 -f flv rtmp://SITE-SUCH-AS-TWITCH/.../STREAM-KEY

  I know these commands are not set up properly for smooth streaming, the result manages to screw up both Twitch’s and Youtube’s player and I will have to figure out how to fix that.

  The problem with this is I don’t think I can stream both the image and the audio at once when creating them on the spot. I have to load one of them from the hard drive. This becomes a problem when trying to react to a command or user chat or anything else that requires live reactions. I also do not want to destroy my hard drive by constantly saving to it.

  As for the python code, what I have tried so far in order to create a video is the following code. This still saves to the HD and is not responsive in realtime, so this is not very useful to me. The video itself is okay, with the one exception that as time passes on, the clock the qr code says versus the video’s clock start to spread apart farther and farther as the video gets closer to the end. I can work around that limitation if it shows up while live streaming.

  def make_frame(t):
  
    img = qrcode.make("Hello! The second is %s!" % t)
  
    return numpy.array(img.convert("RGB"))
  
  
  
  clip = mpy.VideoClip(make_frame, duration=120)
  
  clip.write_gif("test.gif",fps=15)
  
  
  
  gifclip = mpy.VideoFileClip("test.gif")
  
  gifclip.set_duration(120).write_videofile("test.mp4",fps=15)

  My goal is to be able to produce something along the psuedo-code of

  original_video = qrcode_generator("I don't know, a clock, pyotp, today's news sources, just anything that can be generated on the fly!")
  
  original_video.overlay_text(0,0,"This is some sample text, the left two are coordinates, the right three are font, size, and color", Times_New_Roman, 12, Blue)
  
  original_video.add_audio(sine_wave_generator(0,180,2)) # frequency min-max, seconds
  
  
  
  # NOTICE - I did not add any time measurements to the actual video itself. The whole point is this is a live stream and not a video clip, so the time frame would be now. The 2 seconds list above is for our psuedo sine wave generator to know how long the audio clip should be, not for the actual streaming library.
  
  
  
  stream.send_to_rtmp_server(original_video) # Doesn't matter if ffmpeg or some native library

  The above example is what I am looking for in terms of video creation in Python and then streaming. I am not trying to create a clip and then stream it later, I am trying to have the program be able to respond to outside events and then update it’s stream to do whatever it wants. It is sort of like a chat bot, but with video instead of text.

  def track_movement(...):
  
    ...
  
    return ...
  
  
  
  original_video = user_submitted_clip(chat.lastVideoMessage)
  
  original_video.overlay_text(0,0,"The robot watches the user's movements and puts a blue square around it.", Times_New_Roman, 12, Blue)
  
  original_video.add_audio(sine_wave_generator(0,180,2)) # frequency min-max, seconds
  
  
  
  # It would be awesome if I could also figure out how to perform advance actions such as tracking movements or pulling a face out of a clip and then applying effects to it on the fly. I know OpenCV can track movements and I hear that it can work with streams, but I cannot figure out how that works. Any help would be appreciated! Thanks!

  Because I forgot to add the imports, here are some useful imports I have in my file !

  import pyotp
  
  import qrcode
  
  from io import BytesIO
  
  from moviepy import editor as mpy

  The library, pyotp, is for generating one time pad authenticator codes, qrcode is for the qr codes, BytesIO is used for virtual files, and moviepy is what I used to generate the GIF and MP4. I believe BytesIO might be useful for piping data to the streaming service, but how that happens, depends entirely on how data is sent to the service, whether it be ffmpeg over command line (from subprocess import Popen, PIPE) or it be a native library.