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• Something wrong with recording video and merging audio and video by using FFmpeg

  16 mars 2014, par VaFancy

  At the beginning, I have to admit that I am a newbie in FFmpeg and also raspbian. Unfortunately, I need to solve this problem as soon as possible.

  I tried to record videos on my Raspberry PI by using PIcamera and USB webcam separately. When I used command :

  ffmpeg -t 10 -f video4linux2 -r 25 -i /dev/video0 out.h264

  However, something was very wrong because there was nothing saved in that video file. I copy the log.

  [video4linux2,v4l2 @ 0x267ad80] The driver changed the time per frame from 1/25 to 1/30
  
  Input #0, video4linux2,v4l2, from '/dev/video0':
  
    Duration: N/A, start: 37562.908020, bitrate: 147456 kb/s
  
      Stream #0:0: Video: rawvideo (YUY2 / 0x32595559), yuyv422, 640x480, 147456 kb/s, 30 fps, 30 tbr, 1000k tbn, 1000k tbc
  
  File 'out.h264' already exists. Overwrite ? [y/N] y
  
  No pixel format specified, yuv422p for H.264 encoding chosen.
  
  Use -pix_fmt yuv420p for compatibility with outdated media players.
  
  [libx264 @ 0x267ca60] using cpu capabilities: ARMv6
  
  [libx264 @ 0x267ca60] profile High 4:2:2, level 3.0, 4:2:2 8-bit
  
  Output #0, h264, to 'out.h264':
  
    Metadata:
  
      encoder         : Lavf55.34.101
  
      Stream #0:0: Video: h264 (libx264), yuv422p, 640x480, q=-1--1, 90k tbn, 25 tbc
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (rawvideo -> libx264)
  
  Press [q] to stop, [?] for help
  
  DTS 140730117085, next:18648459 st:0 invalid dropping00.00 bitrate=N/A    
  
  PTS 140730117085, next:18648459 invalid dropping st:0
  
  DTS 140740452429, next:6538221 st:0 invalid dropping:00.00 bitrate=N/A    
  
  PTS 140740452429, next:6538221 invalid dropping st:0
  
  frame=   44 fps=1.1 q=28.0 size=      18kB time=00:00:00.04 bitrate=3712.6kbits/
  
  frame=   45 fps=0.8 q=28.0 size=      22kB time=00:00:00.08 bitrate=2262.8kbits/
  
  frame=   46 fps=0.7 q=28.0 size=      27kB time=00:00:00.12 bitrate=1813.4kbits/
  
  frame=   47 fps=0.6 q=28.0 size=      30kB time=00:00:00.16 bitrate=1559.2kbits/
  
  frame=   48 fps=0.5 q=28.0 size=      34kB time=00:00:00.20 bitrate=1397.7kbits/
  
  frame=   49 fps=0.5 q=28.0 size=      39kB time=00:00:00.24 bitrate=1324.3kbits/
  
  frame=   50 fps=0.4 q=28.0 size=      42kB time=00:00:00.28 bitrate=1241.7kbits/ 

  As you can see, the size of each frame was only few kb and fps was so low. Thus each frame was taken by using a long time(4s in this case). Then I test my USB webcam by using command :

  ffplay -f video4linux2 /dev/video0

  The video stream which displayed on the screen was so unnatural. It looked like there was such a long delay between each frame. So the video was hardly moved. The log I have copied :

  pi@raspberrypi ~ $ ffplay -f video4linux2 /dev/video0
  
  avplay version 0.8.6-6:0.8.6-1+rpi1, Copyright (c) 2003-2013 the Libav developers
  
    built on Mar 31 2013 13:58:10 with gcc 4.6.3
  
  [video4linux2 @ 0x1ad83c0] Estimating duration from bitrate, this may be inaccurate
  
  Input #0, video4linux2, from '/dev/video0':
  
    Duration: N/A, start: 36107.154598, bitrate: 147456 kb/s
  
      Stream #0.0: Video: rawvideo, yuyv422, 640x480, 147456 kb/s, 30 tbr, 1000k tbn, 30 tbc
  
  [avsink @ 0x1ad9960] auto-inserting filter 'auto-inserted scaler 0' between the filter 'src' and the filter 'out'
  
  [scale @ 0x1ad9d20] w:640 h:480 fmt:yuyv422 -> w:640 h:480 fmt:yuv420p flags:0x4
  
  36163.33 A-V:  0.000 s:0.0 aq=    0KB vq= 3000KB sq=    0B f=8/8 

  The final problem is that no sound was added in video file when I tried to add some in. I used the command :

  avconv -i /home/pi/Desktop/test.mp4 -i /home/pi/Desktop/test.mp3 -map 0:0 -vcodec copy -map 1:0 -acodec copy output.h264

  I have checked separately that the video and audio were both able to play. However the merged file only had video but no audio. The log is shown below, it seems that the process was succeed.

  pi@raspberrypi ~ $ avconv -i /home/pi/Desktop/test.mp4 -i /home/pi/Desktop/test.mp3 -map 0:0 -vcodec copy -map 1:0 -acodec copy output.h264
  
  avconv version 0.8.6-6:0.8.6-1+rpi1, Copyright (c) 2000-2013 the Libav developers
  
    built on Mar 31 2013 13:58:10 with gcc 4.6.3
  
  [h264 @ 0x1aa6680] max_analyze_duration reached
  
  [h264 @ 0x1aa6680] Estimating duration from bitrate, this may be inaccurate
  
  Input #0, h264, from '/home/pi/Desktop/test.mp4':
  
    Duration: N/A, bitrate: N/A
  
      Stream #0.0: Video: h264 (High), yuv420p, 640x480, 25 fps, 25 tbr, 1200k tbn, 2400k tbc
  
  [mp3 @ 0x1aaa120] Estimating duration from bitrate, this may be inaccurate
  
  Input #1, mp3, from '/home/pi/Desktop/test.mp3':
  
    Metadata:
  
      title           : We Are Legend (Original Mix)
  
      artist          : Dimitri Vegas & Like Mike & Steve Aoki
  
      album           : SPINNIN RECORDS
  
      encoded_by      : iTunes 11.1.5
  
      TBP             : 128
  
      genre           : Progressive House
  
      date            : 2013
  
    Duration: 00:00:11.14, start: 0.000000, bitrate: 191 kb/s
  
      Stream #1.0: Audio: mp3, 48000 Hz, stereo, s16, 192 kb/s
  
  File 'output.h264' already exists. Overwrite ? [y/N] y
  
  Output #0, h264, to 'output.h264':
  
    Metadata:
  
      encoder         : Lavf53.21.1
  
      Stream #0.0: Video: libx264, yuv420p, 640x480, q=2-31, 90k tbn, 1200k tbc
  
      Stream #0.1: Audio: libmp3lame, 48000 Hz, stereo, 192 kb/s
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (copy)
  
    Stream #1:0 -> #0:1 (copy)
  
  Press ctrl-c to stop encoding
  
  frame=  144 fps=  0 q=-1.0 Lsize=    1684kB time=5.06 bitrate=2724.7kbits/s    
  
  video:1566kB audio:119kB global headers:0kB muxing overhead 0.000000%

  Could somebody helps me, please. And sorry for my bad english.

  BTW : When I finally captured 200 frames, it turns out all of them were green.

• VP8 Codec SDK "Aylesbury" Release

  28 octobre 2010, par noreply@blogger.com (John Luther)

  Today we’re making available "Aylesbury," our first named release of libvpx, the VP8 codec SDK. VP8 is the video codec used in WebM. Note that the VP8 specification has not changed, only the SDK.

  What’s an Aylesbury ? It’s a breed of duck. We like ducks, so we plan to use duck-related names for each major libvpx release, in alphabetical order. Our goal is to have one named release of libvpx per calendar quarter, each with a theme.

  You can download the Aylesbury libvpx release from our Downloads page or check it out of our Git repository and build it yourself. In the coming days Aylesbury will be integrated into all of the WebM project components (DirectShow filters, QuickTime plugins, etc.). We encourage anyone using our components to upgrade to the Aylesbury releases.

  For Aylesbury the theme was faster decoder, better encoder. We used our May 19, 2010 launch release of libvpx as the benchmark. We’re very happy with the results (see graphs below) :
  

  • 20-40% (average 28%) improvement in libvpx decoder speed
  • Over 7% overall PSNR improvement (6.3% SSIM) in VP8 "best" quality encoding mode, and up to 60% improvement on very noisy, still or slow moving source video.

  

  
  

  

  
  

  

  
  

  The main improvements to the decoder are :
  

  • Single-core assembly "hot spot" optimizations, including improved vp8_sixtap_predict() and SSE2 loopfilter functions
  • Threading improvements for more efficient use of multiple processor cores
  • Improved memory handling and reduced footprint
  • Combining IDCT and reconstruction steps
  • SSSE3 usage in functions where appropriate

  On the encoder front, we concentrated on clips in the 30-45 dB range and saw the biggest gains in higher-quality source clips (greater that 38 dB), low to medium-motion clips, and clips with noisy source material. Many code contributions made this possible, but a few of the highlights were :
  

  • Adaptive width and strength alternate reference frame noise suppression filter with optional motion compensation.
  • Transform improvements (improved accuracy and reduction in round trip error)
  • Trellis-based quantized coefficient optimization
  • Two-pass rate control and quantizer changes
  • Rate distortion changes
  • Zero bin and rounding changes
  • Work on MB-level quality control and bit allocation

  We’re targeting Q1 2011 for the next named libvpx release, which we’re calling Bali. The theme for that release will be faster encoder. We are constantly working on improvements to video quality in the encoder, so after Aylesbury we won’t tie that work to specific named releases.

  WebM at Streaming Media West

  Members of the WebM project will discuss Aylesbury during a session at the Streaming Media West conference on November 3rd (session C203 : WebM Open Video Project Update). For more information, visit www.streamingmedia.com/west.

  John Luther is Product Manager of the WebM Project.

  

• Cohort Analysis 101 : How-To, Examples & Top Tools

  13 novembre 2023, par Erin — Analytics Tips

  Imagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.

  The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.

  With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.

  Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024. 

  What is cohort analysis ?

  By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.

  Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.

  Why does cohort analysis matter ?

  In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.

  An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories. 

  Types of cohort analysis

  There are a few different types of notable cohorts : 

  1. Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
  2. Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods. 
  3. Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement. 
  4. Demographic cohorts share common demographic characteristics like age, gender, education level and income. 
  5. Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
  6. Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions. 
  7. Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention. 
  8. Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users). 

  Best practices for conducting a cohort analysis 

  So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.

  

  1. Preparing for cohort analysis : 

  • • First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
    • Choosing the right time frame will help you compare short-term vs. long-term data trends. 

  2. Creating effective cohorts : 

  • • Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise. 
    • It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.

  3. Performing cohort analysis :

  • • • Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates. 
      • Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website ? Do they have certain preferences ? Why ? 

  4. Visualising and interpreting data :

  • • Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
    • Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean. 

  5. Continue improving :

  • • User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date. 
    • Encourage iterative analysis optimisation based on your findings. 

  

  The top cohort analysis tools for 2024

  In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.

  1. Matomo

  

  Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time. 

  You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts. 

  Key features

  • Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
    • All core session and conversion metrics are also available in the Cohorts report.
  • Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour. 
  • Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when. 
  • Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts. 
  • Create cohorts based on events or interactions with your website. 
  • Intuitive, colour-coded data visualisation, so you can easily spot patterns.

  Pros

  • No setup is needed if you use the JavaScript tracker
  • You can fetch cohort without any limit
  • 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling

  Cons

  • Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
  • Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.

  Price : 

  • Matomo Cloud : 21-day free trial, then starts at $19 per month (includes Cohorts).
  • Matomo On-Premise : Free to self-host ; Cohorts plugin : 30-day free trial, then $99 per year.

  2. Mixpanel

  

  Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users. 

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
  • Create interactive dashboards, charts and graphs to visualise data.
  • Mixpanel provides retention analysis tools to see how often users return to your product over time. 
  • Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc. 
  • Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.

  Pros

  • Easily export cohort analysis data for further analysis
  • Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product

  Cons

  • With the free Mixpanel plan, you can’t save cohorts for future use
  • Enterprise-level pricing is expensive
  • Time-consuming cohort creation process

  Price : Free basic version. The growth version starts at £16/month.

  3. Amplitude

  

  Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Conduct behavioural, time-based and retention analyses.
  • Create custom reports with custom data.
  • Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.

  Pros

  • Highly customisable and flexible
  • Quick and simple setup

  Cons

  • Steep learning curve — requires significant training 
  • Slow loading speed
  • High price point compared to other tools

  Price : Free basic version. Plus version starts at £40/month (billed annually).

  4. Kissmetrics

  

  Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation. 

  Key features

  • Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
  • The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points. 
  • Create and optimise conversion funnels.
  • Customise events, user properties, funnels, segments, cohorts and more.

  Pros

  • Powerful data visualisation options
  • Highly customisable

  Cons

  • Difficult to install
  • Not well-suited for small businesses
  • Limited integration with other tools

  Price : Starting at £21/month for 10k events (billed monthly).

  Improve your cohort analysis with Matomo

  When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards. 

  If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.