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  • RTMP server with OpenCV (python)

    12 février 2024, par Overnout

    I'm trying to process an RTMP stream in Python, using OpenCV2 but I'm not able to get OpenCV to capture it (i.e. act as RTMP server).

    


    I can run FFmpeg/FFplay from the command line and receive the stream successfully.
What could cause OpenCV to fail opening the stream in listening mode ?

    


    Here is my code :

    


    import cv2

cap = cv2.VideoCapture("rtmp://0.0.0.0:8000/live", cv2.CAP_FFMPEG)

if not cap.isOpened():
    print("Cannot open video source")
    exit()


    


    And the output :

    


    [tcp @ 00000192c490d640] Connection to tcp://0.0.0.0:8000 failed: Error number -138 occurred
[rtmp @ 00000192c490d580] Cannot open connection tcp://0.0.0.0:8000 
Cannot open video source


    


    edit2 : Output with debug logging turned on :

    


    output of the python script with debug logging on:
[DEBUG:0@0.017] global videoio_registry.cpp:218 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Builtin backends(9): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[DEBUG:0@0.026] global videoio_registry.cpp:242 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Available backends(9): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[ INFO:0@0.031] global videoio_registry.cpp:244 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Enabled backends(9, sorted by priority): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[ WARN:0@0.037] global cap.cpp:132 cv::VideoCapture::open VIDEOIO(FFMPEG): trying capture filename='rtmp://192.168.254.101:8000/live' ...
[ INFO:0@0.040] global backend_plugin.cpp:383 cv::impl::getPluginCandidates Found 2 plugin(s) for FFMPEG
[ INFO:0@0.043] global plugin_loader.impl.hpp:67 cv::plugin::impl::DynamicLib::libraryLoad load C:\Users\me\src\opencv\.venv\Lib\site-packages\cv2\opencv_videoio_ffmpeg490_64.dll => OK
[ INFO:0@0.047] global backend_plugin.cpp:50 cv::impl::PluginBackend::initCaptureAPI Found entry: 'opencv_videoio_capture_plugin_init_v1'
[ INFO:0@0.049] global backend_plugin.cpp:169 cv::impl::PluginBackend::checkCompatibility Video I/O: initialized 'FFmpeg OpenCV Video I/O Capture plugin': built with OpenCV 4.9 (ABI/API = 1/1), current OpenCV version is '4.9.0' (ABI/API = 1/1)
[ INFO:0@0.055] global backend_plugin.cpp:69 cv::impl::PluginBackend::initCaptureAPI Video I/O: plugin is ready to use 'FFmpeg OpenCV Video I/O Capture plugin'
[ INFO:0@0.058] global backend_plugin.cpp:84 cv::impl::PluginBackend::initWriterAPI Found entry: 'opencv_videoio_writer_plugin_init_v1'
[ INFO:0@0.061] global backend_plugin.cpp:169 cv::impl::PluginBackend::checkCompatibility Video I/O: initialized 'FFmpeg OpenCV Video I/O Writer plugin': built with OpenCV 4.9 (ABI/API = 1/1), current OpenCV version is '4.9.0' (ABI/API = 1/1)
[ INFO:0@0.065] global backend_plugin.cpp:103 cv::impl::PluginBackend::initWriterAPI Video I/O: plugin is ready to use 'FFmpeg OpenCV Video I/O Writer plugin'
[tcp @ 00000266b2f0d0c0] Connection to tcp://192.168.254.101:8000 failed: Error number -138 occurred
[rtmp @ 00000266b2f0cfc0] Cannot open connection tcp://192.168.254.101:8000
[ WARN:0@5.630] global cap.cpp:155 cv::VideoCapture::open VIDEOIO(FFMPEG): can't create capture
[DEBUG:0@5.632] global cap.cpp:225 cv::VideoCapture::open VIDEOIO: choosen backend does not work or wrong. Please make sure that your computer support chosen backend and OpenCV built with right flags.
Cannot open video source
[ INFO:1@5.661] global plugin_loader.impl.hpp:74 cv::plugin::impl::DynamicLib::libraryRelease unload C:\Users\me\src\opencv\.venv\Lib\site-packages\cv2\opencv_videoio_ffmpeg490_64.dll


    


    Here is the output of cv2.getBuildInformation()

    


    General configuration for OpenCV 4.9.0 =====================================
  Version control:               4.9.0

  Platform:
    Timestamp:                   2023-12-31T11:21:12Z
    Host:                        Windows 10.0.17763 AMD64
    CMake:                       3.24.2
    CMake generator:             Visual Studio 14 2015
    CMake build tool:            MSBuild.exe
    MSVC:                        1900
    Configuration:               Debug Release

  CPU/HW features:
    Baseline:                    SSE SSE2 SSE3
      requested:                 SSE3
    Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2
      requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
      SSE4_1 (16 files):         + SSSE3 SSE4_1
      SSE4_2 (1 files):          + SSSE3 SSE4_1 POPCNT SSE4_2
      FP16 (0 files):            + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
      AVX (8 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
      AVX2 (36 files):           + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2

  C/C++:
    Built as dynamic libs?:      NO
    C++ standard:                11
    C++ Compiler:                C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe  (ver 19.0.24247.2)
    C++ flags (Release):         /DWIN32 /D_WINDOWS /W4 /GR  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP  /O2 /Ob2 /DNDEBUG 
    C++ flags (Debug):           /DWIN32 /D_WINDOWS /W4 /GR  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP  /Zi /Ob0 /Od /RTC1 
    C Compiler:                  C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe
    C flags (Release):           /DWIN32 /D_WINDOWS /W3  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /MP   /O2 /Ob2 /DNDEBUG 
    C flags (Debug):             /DWIN32 /D_WINDOWS /W3  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /MP /Zi /Ob0 /Od /RTC1 
    Linker flags (Release):      /machine:x64  /NODEFAULTLIB:atlthunk.lib /INCREMENTAL:NO  /NODEFAULTLIB:libcmtd.lib /NODEFAULTLIB:libcpmtd.lib /NODEFAULTLIB:msvcrtd.lib
    Linker flags (Debug):        /machine:x64  /NODEFAULTLIB:atlthunk.lib /debug /INCREMENTAL  /NODEFAULTLIB:libcmt.lib /NODEFAULTLIB:libcpmt.lib /NODEFAULTLIB:msvcrt.lib
    ccache:                      NO
    Precompiled headers:         YES
    Extra dependencies:          wsock32 comctl32 gdi32 ole32 setupapi ws2_32
    3rdparty dependencies:       libprotobuf ade ittnotify libjpeg-turbo libwebp libpng libtiff libopenjp2 IlmImf zlib ippiw ippicv

  OpenCV modules:
    To be built:                 calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo python3 stitching video videoio
    Disabled:                    java world
    Disabled by dependency:      -
    Unavailable:                 python2 ts
    Applications:                -
    Documentation:               NO
    Non-free algorithms:         NO

  Windows RT support:            NO

  GUI:                           WIN32UI
    Win32 UI:                    YES
    VTK support:                 NO

  Media I/O: 
    ZLib:                        build (ver 1.3)
    JPEG:                        build-libjpeg-turbo (ver 2.1.3-62)
      SIMD Support Request:      YES
      SIMD Support:              NO
    WEBP:                        build (ver encoder: 0x020f)
    PNG:                         build (ver 1.6.37)
    TIFF:                        build (ver 42 - 4.2.0)
    JPEG 2000:                   build (ver 2.5.0)
    OpenEXR:                     build (ver 2.3.0)
    HDR:                         YES
    SUNRASTER:                   YES
    PXM:                         YES
    PFM:                         YES

  Video I/O:
    DC1394:                      NO
    FFMPEG:                      YES (prebuilt binaries)
      avcodec:                   YES (58.134.100)
      avformat:                  YES (58.76.100)
      avutil:                    YES (56.70.100)
      swscale:                   YES (5.9.100)
      avresample:                YES (4.0.0)
    GStreamer:                   NO
    DirectShow:                  YES
    Media Foundation:            YES
      DXVA:                      YES

  Parallel framework:            Concurrency

  Trace:                         YES (with Intel ITT)

  Other third-party libraries:
    Intel IPP:                   2021.11.0 [2021.11.0]
           at:                   D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-build/3rdparty/ippicv/ippicv_win/icv
    Intel IPP IW:                sources (2021.11.0)
              at:                D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-build/3rdparty/ippicv/ippicv_win/iw
    Lapack:                      NO
    Eigen:                       NO
    Custom HAL:                  NO
    Protobuf:                    build (3.19.1)
    Flatbuffers:                 builtin/3rdparty (23.5.9)

  OpenCL:                        YES (NVD3D11)
    Include path:                D:/a/opencv-python/opencv-python/opencv/3rdparty/include/opencl/1.2
    Link libraries:              Dynamic load

  Python 3:
    Interpreter:                 C:/hostedtoolcache/windows/Python/3.7.9/x64/python.exe (ver 3.7.9)
    Libraries:                   C:/hostedtoolcache/windows/Python/3.7.9/x64/libs/python37.lib (ver 3.7.9)
    numpy:                       C:/hostedtoolcache/windows/Python/3.7.9/x64/lib/site-packages/numpy/core/include (ver 1.17.0)
    install path:                python/cv2/python-3

  Python (for build):            C:\hostedtoolcache\windows\Python\3.7.9\x64\python.exe

  Java:                          
    ant:                         NO
    Java:                        YES (ver 1.8.0.392)
    JNI:                         C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include/win32 C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include
    Java wrappers:               NO
    Java tests:                  NO

  Install to:                    D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-install
-----------------------------------------------------------------


    


    edit : Receiving the stream with ffplay from command line :

    


    >ffplay.exe -i "rtmp://0.0.0.0:8000/live"  -listen 1 -f flv
ffplay version 2024-02-04-git-7375a6ca7b-full_build-www.gyan.dev Copyright (c) 2003-2024 the FFmpeg developers
  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --pkg-config=pkgconf --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-dxva2 --enable-d3d11va --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      58. 36.101 / 58. 36.101
  libavcodec     60. 38.100 / 60. 38.100
  libavformat    60. 20.100 / 60. 20.100
  libavdevice    60.  4.100 / 60.  4.100
  libavfilter     9. 17.100 /  9. 17.100
  libswscale      7.  6.100 /  7.  6.100
  libswresample   4. 13.100 /  4. 13.100
  libpostproc    57.  4.100 / 57.  4.100
[rtmp @ 0000018a564ed340] Unexpected stream , expecting livef=0/0
    Last message repeated 1 times
Input #0, flv, from 'rtmp://0.0.0.0:8000/live':KB sq=    0B f=0/0
  Metadata:
    fileSize        : 0
    audiochannels   : 2
    2.1             : false
    3.1             : false
    4.0             : false
    4.1             : false
    5.1             : false
    7.1             : false
    encoder         : obs-output module (libobs version 30.0.2)
  Duration: 00:00:00.00, start: 0.000000, bitrate: N/A
  Stream #0:0: Audio: aac (LC), 48000 Hz, stereo, fltp, 163 kb/s
  Stream #0:1: Video: h264 (Constrained Baseline), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], 2560 kb/s, 30 fps, 30 tbr, 1k tbn
   7.54 A-V: -0.024 fd=  18 aq=   24KB vq=  498KB sq=    0B f=0/0


    


  • 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.

    A line graph depicting product usage cohort data with a blue line for new users and a green line for power users.

    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. 
    wrench trying to hammer in a nail, and a hammer trying to screw in a screw to a piece of wood

    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

    A screenshot of a cohorts graph in 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

    A screenshot of a cohorts graph in 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

    A screenshot of a cohorts graph in 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.

  • Display image every X second and during Y second in ffmpeg

    29 juin 2016, par Ahmad Ali Mukashaty

    I want to stream video with gif image (myimage.gif) but I want to display it every X minute (every 10 minutes) and during this time I want to make it loops Y time and then disappear (fade out).

    or

    dispaly myimage.gif every X minute and still appear during Y minute and then disappear (disappear after 5 minute for example )

    I want to make this every 10 minutes until video finishes
    how can I do it ?

    ffmpeg -re -i test.mp4 -i logo.png -i myimage.gif -filter_complex
    "[0][1]overlay=0:0[a];[a][2]overlay=0:H-90,scale=1920:1080" -f mpegts
    udp://127.0.0.1:port