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Des sites réalisés avec MediaSPIP
2 mai 2011, parCette page présente quelques-uns des sites fonctionnant sous MediaSPIP.
Vous pouvez bien entendu ajouter le votre grâce au formulaire en bas de page. -
Déploiements possibles
31 janvier 2010, parDeux types de déploiements sont envisageable dépendant de deux aspects : La méthode d’installation envisagée (en standalone ou en ferme) ; Le nombre d’encodages journaliers et la fréquentation envisagés ;
L’encodage de vidéos est un processus lourd consommant énormément de ressources système (CPU et RAM), il est nécessaire de prendre tout cela en considération. Ce système n’est donc possible que sur un ou plusieurs serveurs dédiés.
Version mono serveur
La version mono serveur consiste à n’utiliser qu’une (...) -
Les tâches Cron régulières de la ferme
1er décembre 2010, parLa gestion de la ferme passe par l’exécution à intervalle régulier de plusieurs tâches répétitives dites Cron.
Le super Cron (gestion_mutu_super_cron)
Cette tâche, planifiée chaque minute, a pour simple effet d’appeler le Cron de l’ensemble des instances de la mutualisation régulièrement. Couplée avec un Cron système sur le site central de la mutualisation, cela permet de simplement générer des visites régulières sur les différents sites et éviter que les tâches des sites peu visités soient trop (...)
Sur d’autres sites (7356)
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Linear Attribution Model : What Is It and How Does It Work ?
16 février 2024, par ErinWant a more in-depth way to understand the effectiveness of your marketing campaigns ? Then, the linear attribution model could be the answer.
Although you can choose from several different attribution models, a linear model is ideal for giving value to every touchpoint along the customer journey. It can help you identify your most effective marketing channels and optimise your campaigns.
So, without further ado, let’s explore what a linear attribution model is, when you should use it and how you can get started.
What is a linear attribution model ?
A linear attribution model is a multi-touch method of marketing attribution where equal credit is given to each touchpoint. Every marketing channel used across the entire customer journey gets credit, and each is considered equally important.
So, if a potential customer has four interactions before converting, each channel gets 25% of the credit.
Let’s look at how linear attribution works in practice using a hypothetical example of a marketing manager, Sally, who is looking for an alternative to Google Analytics.
Sally starts her conversion path by reading a Matomo article comparing Matomo to Google Analytics she finds when searching on Google. A few days later she signs up for a webinar she saw on Matomo’s LinkedIn page. Two weeks later, Sally gets a sign-off from her boss and decides to go ahead with Matomo. She visits the website and starts a free trial by clicking on one of the paid Google Ads.
Using a linear attribution model, we credit each of the channels Sally uses (organic traffic, organic social, and paid ads), ensuring no channel is overlooked in our marketing analysis.
Are there other types of attribution models ?
Absolutely. There are several common types of attribution models marketing managers can use to measure the impact of channels in different ways.
- First interaction : Also called a first-touch attribution model, this method gives all the credit to the first channel in the customer journey. This model is great for optimising the top of your sales funnel.
- Last interaction : Also called a last-touch attribution model, this approach gives all the credit to the last channel the customer interacts with. It’s a great model for optimising the bottom of your marketing funnel.
- Last non-direct interaction : This attribution model excludes direct traffic and credits the previous touchpoint. This is a fantastic alternative to a last-touch attribution model, especially if most customers visit your website before converting.
- Time decay attribution model : This model adjusts credit according to the order of the touchpoints. Those nearest the conversion get weighted the highest.
- Position-based attribution model : This model allocates 40% of the credit to the first and last touchpoints and splits the remaining 20% evenly between every other interaction.
Why use a linear attribution model ?
Marketing attribution is vital if you want to understand which parts of your marketing strategy are working. All of the attribution models described above can help you achieve this to some degree, but there are several reasons to choose a linear attribution model in particular.
It uses multi-touch attribution
Unlike single-touch attribution models like first and last interaction, linear attribution is a multi-touch attribution model that considers every touchpoint. This is vital to get a complete picture of the modern customer journey, where customers interact with companies between 20 and 500 times.
Single-touch attribution models can be misleading by giving conversion credit to a single channel, especially if it was the customer’s last use. In our example above, Sally’s last interaction with our brand was through a paid ad, but it was hardly the most important.
It’s easy to understand
Attribution models can be complicated, but linear attribution is easy to understand. Every touchpoint gets the same credit, allowing you to see how your entire marketing function works. This simplicity also makes it easy for marketers to take action.
It’s great for identifying effective marketing channels
Because linear attribution is one of the few models that provides a complete view of the customer journey, it’s easy to identify your most common and influential touchpoints.
It accounts for the top and bottom of your funnel, so you can also categorise your marketing channels more effectively and make more informed decisions. For example, PPC ads may be a more common bottom-of-the-full touchpoint and should, therefore, not be used to target broad, top-of-funnel search terms.
Are there any reasons not to use linear attribution ?
Linear attribution isn’t perfect. Like all attribution models, it has its weaknesses. Specifically, linear attribution can be too simple, dilute conversion credit and unsuitable for long sales cycles.
It can be too simple
Linear attribution lacks nuance. It only considers touchpoints while ignoring other factors like brand image and your competitors. This is true for most attribution models, but it’s still important to point it out.
It can dilute conversion credit
In reality, not every touchpoint impacts conversions to the same extent. In the example above, the social media post promoting the webinar may have been the most effective touchpoint, but we have no way of measuring this.
The risk with using a linear model is that credit can be underestimated and overestimated — especially if you have a long sales cycle.
It’s unsuitable for very long sales cycles
Speaking of long sales cycles, linear attribution models won’t add much value if your customer journey contains dozens of different touchpoints. Credit will get diluted to the point where analysis becomes impossible, and the model will also struggle to measure the precise ways certain touchpoints impact conversions.
Should you use a linear attribution model ?
A linear attribution model is a great choice for any company with shorter sales cycles or a reasonably straightforward customer journey that uses multiple marketing channels. In these cases, it helps you understand the contribution of each touchpoint and find your best channels.
It’s also a practical choice for small businesses and startups that don’t have a team of data scientists on staff or the budget to hire outside help. Because it’s so easy to set up and understand, anyone can start generating insights using this model.
How to set up a linear attribution model
Are you sold on the idea of using a linear attribution model ? Then follow the steps below to get started :
Choose a marketing attribution tool
Given the market is worth $3.1 billion, you won’t be surprised to learn there are plenty of tools to choose from. But choose carefully. The tool you pick can significantly impact your success with attribution modelling.
Take Google Analytics, for instance. While GA4 offers several marketing attribution models for free, including linear attribution, it lacks accuracy due to cookie consent rejection and data sampling.
Accurate marketing attribution is included as a feature in Matomo Cloud and is available as a plugin for Matomo On-Premise users. We support a full range of attribution models that use 100% accurate data because we don’t use data sampling, and cookie consent isn’t an issue (with the exception of Germany and the UK). That means you can trust our insights.
Matomo’s marketing attribution is available out of the box, and we also provide access to raw data, allowing you to develop your custom attribution model.
Collect data
The quality of your marketing attribution also depends on the quality and quantity of your data. It’s why you need to avoid a platform that uses data sampling.
This should include :
- General data from your analytics platform, like pages visited and forms filled
- Goals and conversions, which we’ll discuss in more detail in the next step
- Campaign tracking data so you can monitor the behaviour of traffic from different referral channels
- Behavioural data from features like Heatmaps or Session Recordings
Set up goals and conversions
You can’t assign conversion values to customer journey touchpoints if you don’t have conversion goals in place. That’s why the next step of the process is to set up conversion tracking in your web analytics platform.
Depending on your type of business and the product you sell, conversions could take one of the following forms :
- A product purchase
- Signing up for a webinar
- Downloading an ebook
- Filling in a form
- Starting a free trial
Setting up these kinds of goals is easy if you use Matomo.
Just head to the Goals section of the dashboard, click Manage Goals and then click the green Add A New Goal button.
Fill in the screen below, and add a Goal Revenue at the bottom of the page. Doing so will mean Matomo can automatically calculate the value of each touchpoint when using your attribution model.
If your analytics platform allows it, make sure you also set up Event Tracking, which will allow you to analyse how many users start to take a desired action (like filling in a form) but never complete the task.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
Test and validate
As we’ve explained, linear attribution is a great model in some scenarios, but it can fall short if you have a long or complex sales funnel. Even if you’re sure it’s the right model for your company, testing and validating is important.
Ideally, your chosen attribution tool should make this process pretty straightforward. For example, Matomo’s Marketing Attribution feature makes comparing and contrasting three different attribution models easy.
Here we compare the performance of three attribution models—linear, first-touch, and last-non-direct—in Matomo’s Marketing Attribution dashboard, providing straightforward analysis.
If you think linear attribution accurately reflects the value of your channels, you can start to analyse the insights it generates. If not, then consider using another attribution model.
Don’t forget to take action from your marketing efforts, either. Linear attribution helps you spot the channels that contribute most to conversions, so allocate more resources to those channels and see if you can improve your conversion rate or boost your ROI.
Make the most of marketing attribution with Matomo
A linear attribution model lets you measure every touchpoint in your customer journey. It’s an easy attribution model to start with and lets you identify and optimise your most effective marketing channels.
However, accurate data is essential if you want to benefit the most from marketing attribution data. If your web analytics solution doesn’t play nicely with cookies or uses sampled data, then your linear model isn’t going to tell you the whole story.
That’s why over 1 million sites trust Matomo’s privacy-focused web analytics, ensuring accurate data for a comprehensive understanding of customer journeys.
Now you know what linear attribution modelling is, start employing the model today by signing up for a free 21-day trial, no credit card required.
Try Matomo for Free
21 day free trial. No credit card required.
-
RTMP server with OpenCV (python)
12 février 2024, par OvernoutI'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


-
Split video with ffmpeg segment option is missing frame
9 février 2024, par DanI’m trying to get the ffmpeg “segment” option to split my video into segments at the Iframes. I'm using ffmpeg V6.1.1.


First I added time stamps to each frame of my video so that when it plays, I can see exactly which frame is being displayed. I used this command :


ffmpeg -i In.mp4 -vf "drawtext=fontfile='C :\Windows\Fonts\Arial.ttf' : text='%frame_num :~ %pts':fontsize=200 : r=25 : x=(w-tw)/2 : y=h-(2*lh) : fontcolor=white : box=1 : boxcolor=0x00000099" -y Out.mp4


Then I used ffprobe to confirm that the video is 30 FPS and the Iframes are as follows :


0.000000
4.933333
10.000000
11.533333
18.866667
24.966667


Based on these Iframe times, I’d expect the following segments :







 Start Frame 

Start Time 

End Frame 

End Time 







 0 

0 

147 

4.900000 




 148 

4.933333 

299 

9.966667 




 300 

10.000000 

345 

11.500000 




 346 

11.533333 

565 

18.833334 




 566 

18.866667 

748 

24.933334 




 749 

24.966667 

867 

28.906667 









When I use ffmpeg to split the video into segments with the following command, I get six files as expected :


ffmpeg -i Out.mp4 -f segment -c copy -reset_timestamps 1 -map 0 "Out %d.mp4"


When I play the segments, they are all correct except the first segment file (Out 0.mp4). It seems to be missing the last frame. It contains frames 0 to 146 (4.866667 sec) but should also include frame 147 (4.9 sec). All the other segment files are as expected.


I’ve tried this on several different mp4 videos and they all are missing the last frame on the first segments.


Any idea why my first segment files is missing the last frame of the segment ?


Could this be an ffmpeg bug ?


Thanks for the help !
Dan


Here is my console session with all output :


C:\> ffprobe Out.mp4
ffprobe version 2023-12-21-git-1e42a48e37-full_build-www.gyan.dev Copyright (c) 2007-2023 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. 36.100 / 58. 36.100
 libavcodec 60. 36.100 / 60. 36.100
 libavformat 60. 20.100 / 60. 20.100
 libavdevice 60. 4.100 / 60. 4.100
 libavfilter 9. 14.100 / 9. 14.100
 libswscale 7. 6.100 / 7. 6.100
 libswresample 4. 13.100 / 4. 13.100
 libpostproc 57. 4.100 / 57. 4.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'Out.mp4':
 Metadata:
 major_brand : isom
 minor_version : 512
 compatible_brands: isomiso2avc1mp41
 title : Short 4k video sample - 4K Ultra HD (3840x2160)
 date : 2014:05:24 19:00:00
 encoder : Lavf60.20.100
 Duration: 00:00:28.96, start: 0.000000, bitrate: 3181 kb/s
 Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1920x1080 [SAR 1:1 DAR 16:9], 3045 kb/s, 30 fps, 30 tbr, 15360 tbn (default)
 Metadata:
 handler_name : VideoHandler
 vendor_id : [0][0][0][0]
 encoder : Lavc60.36.100 libx264
 Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)
 Metadata:
 handler_name : SoundHandler
 vendor_id : [0][0][0][0]

C:\ ffprobe -loglevel error -skip_frame nokey -select_streams v:0 -show_entries frame=pts_time -of csv=print_section=0 Out.mp4
0.000000,
4.933333
10.000000
11.533333
18.866667
24.966667

C:\ ffmpeg -i Out.mp4 -f segment -c copy -reset_timestamps 1 -map 0 "Out %1d.mp4"
ffmpeg version 2023-12-21-git-1e42a48e37-full_build-www.gyan.dev Copyright (c) 2000-2023 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. 36.100 / 58. 36.100
 libavcodec 60. 36.100 / 60. 36.100
 libavformat 60. 20.100 / 60. 20.100
 libavdevice 60. 4.100 / 60. 4.100
 libavfilter 9. 14.100 / 9. 14.100
 libswscale 7. 6.100 / 7. 6.100
 libswresample 4. 13.100 / 4. 13.100
 libpostproc 57. 4.100 / 57. 4.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'Out.mp4':
 Metadata:
 major_brand : isom
 minor_version : 512
 compatible_brands: isomiso2avc1mp41
 title : Short 4k video sample - 4K Ultra HD (3840x2160)
 date : 2014:05:24 19:00:00
 encoder : Lavf60.20.100
 Duration: 00:00:28.96, start: 0.000000, bitrate: 3181 kb/s
 Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1920x1080 [SAR 1:1 DAR 16:9], 3045 kb/s, 30 fps, 30 tbr, 15360 tbn (default)
 Metadata:
 handler_name : VideoHandler
 vendor_id : [0][0][0][0]
 encoder : Lavc60.36.100 libx264
 Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)
 Metadata:
 handler_name : SoundHandler
 vendor_id : [0][0][0][0]
Stream mapping:
 Stream #0:0 -> #0:0 (copy)
 Stream #0:1 -> #0:1 (copy)
[segment @ 00000195bbc52940] Opening 'Out 0.mp4' for writing
Output #0, segment, to 'Out %1d.mp4':
 Metadata:
 major_brand : isom
 minor_version : 512
 compatible_brands: isomiso2avc1mp41
 title : Short 4k video sample - 4K Ultra HD (3840x2160)
 date : 2014:05:24 19:00:00
 encoder : Lavf60.20.100
 Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1920x1080 [SAR 1:1 DAR 16:9], q=2-31, 3045 kb/s, 30 fps, 30 tbr, 15360 tbn (default)
 Metadata:
 handler_name : VideoHandler
 vendor_id : [0][0][0][0]
 encoder : Lavc60.36.100 libx264
 Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)
 Metadata:
 handler_name : SoundHandler
 vendor_id : [0][0][0][0]
Press [q] to stop, [?] for help
[segment @ 00000195bbc52940] Opening 'Out 1.mp4' for writing
[segment @ 00000195bbc52940] Opening 'Out 2.mp4' for writing
[segment @ 00000195bbc52940] Opening 'Out 3.mp4' for writing
[segment @ 00000195bbc52940] Opening 'Out 4.mp4' for writing
[segment @ 00000195bbc52940] Opening 'Out 5.mp4' for writing
[out#0/segment @ 00000195bc3e8cc0] video:10757kB audio:456kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
size=N/A time=00:00:28.86 bitrate=N/A speed= 322x


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