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• Why Matomo is the top Google Analytics alternative

  17 juin, par Joe

  You probably made the switch to Google Analytics 4 (GA4) when Google stopped collecting Universal Analytics (UA) data in July 2023. Up to that point, UA had long been the default analytics platform, despite its many limitations. 

  This was mostly because everyone loved its free nature and simple setup. A Google account was all you needed — even a free legacy G-Suite account worked perfectly. Looking at the analytics for just about any website was easy.

  That all changed with GA4, which addressed many of UA’s shortcomings by introducing a completely new way to model website data. Unfortunately, this also meant you couldn’t transfer historical data from UA into GA4, leading to more criticism. 

  Then there’s the added cost. GA4 is still free, but its limited functionality encourages you to upgrade to the enterprise version, Google Analytics 360 (GA360). Sure, you get lots of great functionality, less data sampling, and longer data retention periods, but it comes at a hefty price — $50,000 per year, to be exact.

  There are other options, though, and Matomo Analytics is one of the best. It’s an open-source, privacy-centric platform that offers advanced features of GA360 and more. 

  In this article, we’ll compare GA4, GA360, and Matomo and give you what you need to make an informed decision.

  Google Analytics 4 in a nutshell

  Google Analytics 4 is a great tool to use to start learning about web analytics. But soon enough, you’ll likely find that GA4 doesn’t quite cover all of your needs. 

  For example, it can’t provide a detailed view of user experiences, and Google doesn’t offer dedicated support or onboarding. There are other shortcomings, too.

  Data sampling

  Google only processes a selected sample of website activity rather than every individual data point. Rather than looking at the whole picture, it sets a threshold and selects a [hopefully] representative sample for analysis. 

  This inevitably creates gaps in data. Google attempts to fill them in using AI and machine learning, inferring the rest from data patterns. Since the results rely on assumptions and estimates, they aren’t always precise.

  In practical terms, this means that the accuracy of GA4 analysis will likely decline as website traffic increases.

  

  (Image source)

  Data collection limits

  GA4’s 25 million monthly events limit seems like a lot, but they add up quickly. 

  All user interactions are recorded as events, including :

  • Session start : User visits the site.
  • Page view : User loads a page (tracked automatically).
  • First visit : User accesses the site for the first time.
  • User engagement : User stays on a page for a set time period.
  • Scroll : User scrolls past 90% of the page (enhanced measurement).
  • Click : User clicks on any element (links, buttons, etc.).
  • Video start/complete : User starts or completes a video (enhanced measurement).
  • File download : User downloads a file (enhanced measurement).

  For context, consider a website averaging 50 events per session per user. If every user logs on every third day, on average, you’ll need 10,000 individual visitors a month to reach that 25 million. But that’s not the problem. 

  The problem is that collection limits in GA4 affect your ability to capture, secure, and analyse customer data effectively.

  Customisation

  GA4 users also face configuration limits that restrict their customisation options. For example : 

  • Audience limits : Since only 100 audiences are allowed, it’s necessary to combine or optimise segments rather than track too many small groups. 
  • Retention limits : Data retention is limited to only 14 months, so external storage solutions may be necessary in situations where historical data needs to be preserved.
  • Conversion events : GA4 will only track up to 30 conversion events, so it’s best to focus on high-value interactions (e.g., purchases and lead form submissions). 
  • Event-scoped dimensions : Since e-commerce operations are limited to 50 event-scoped dimensions, they need to carefully consider custom dimensions and key metrics. This makes it important to be selective about which product details to track (color, size, discount code, etc.).

  Data privacy

  GA4 isn’t GDPR-compliant out of the box. In fact, Google Analytics 4 is banned in seven EU countries because they believe the way it collects and transfers data violates GDPR.

  Data privacy regulations may or may not be a big concern, depending on where your customers are. However, if some are in the UK or any of the 30 countries that make up the European Economic Area (EEA), you must comply with the General Data Protection Regulation (GDPR). 

  It tells your customers that you don’t respect their data if you don’t. It can also get very expensive.

  Limited attribution models

  Attribution models track how different marketing touchpoints lead to a conversion (such as a purchase, sign-up, or lead generation). They help businesses understand which marketing channels and strategies are most effective in driving results.

  GA4 supports only two of the six standard attribution models previously supported in Universal Analytics. Organisations wanting data-driven or last-click attribution models will find them in Google Analytics. But they’ll need to look elsewhere if they’re going to use any of these models :

  • First click attribution
  • Linear attribution
  • Time decay attribution
  • Position-based attribution (u-shaped)

  GA360 isn’t a solution either

  Fundamentally, GA360 is the same product as GA4, without the above limits and restrictions. For companies that pay $50,000 (or more) each year, the only changes involve how much data is collected, how long it stays and data sampling thresholds.

  Above all, the GDPR-compliance issue remains. That can be a real problem for organisations with operations that collect personal data in the EEA or the UK.

  And the problem could soon be much bigger than just those 31 countries. Many countries currently implementing data privacy laws are modelling their efforts on GDPR, which may rule out both GA4 and GA360.

  

  What makes Matomo the top alternative ?

  No data limits

  One way to overcome all these challenges is to switch to Matomo Analytics. 

  There’s no data sampling and no data collection limits whatsoever with on-premise implementation. Matomo also supports all six attribution models, is open source and fully customisable and complies with GDPR out of the box. 

  Imagine trying to change your business strategy or marketing campaigns if you’re not confident that your data is reliable and accurate.

  It’s no secret that data sampling can negatively affect the accuracy of the data, and inaccurate data can lead to poor decision-making.
  

  With Matomo, there are no limits. We don’t restrict the size of containers within the Tag Manager nor the number of containers or tags within each container. You have more control over your customers’ data. 

  And you get to make your decisions based on all that data. That’s important because data quality is critical for high-impact decisions. 

  Open source

  Open-source software allows anyone to inspect, audit, and improve the source code for security and efficiency. That means no hidden data collection, faster bug fixes, and no vendor lock-in. As a bonus, these things make complying with data privacy laws and regulations easier.

  Matomo can also be modified in any way, which provides unlimited customisation possibilities. There’s also a very active developer community around Matomo, so you don’t have to make changes yourself — you can hire someone who has the technical knowledge and expertise. They can : 

  • Modify tracking scripts for advanced analytics
  • Create custom attribution models, tracking methods and dashboards
  • Integrate Matomo with any system (CRM, eCommerce, CMS, etc.)

  Data ownership

  Matomo’s open-source nature also means full data ownership. No third parties can access the data, and there’s no risk of Google using that data for ads or AI training. Furthermore, Matomo follows privacy-first tracking principles, meaning that there’s :

  • No third-party data sharing
  • Full user consent control
  • Support for cookie-less tracking
  • IP Anonymisation, by default
  • Do Not Track (DNT) support

  All of that underlines the fact that Matomo collects, stores, and tracks data 100% ethically.

  On-premise and cloud-based options

  You can use the Matomo On-Premise web analytics solution if local data privacy laws require that you store data locally. Here’s a helpful tip : many of them do. However, this might not be necessary. 

  Due to GDPR, several countries recognise the EEA as an acceptable storage location for their citizens’ data. That means servers hosted in any of those 30 countries are already compliant in terms of data location. 

  Alternatively, you could embrace modernity and choose Matomo Cloud — our servers are also in Europe. While GA4 and GA360 are cloud-based, Google’s servers are in the US, and that’s a big problem for GDPR.

  

  Comprehensive analytics

  If you need a sophisticated web analytics platform that offers full control of your data and you have privacy concerns, Matomo is a solid choice. 

  It has built-in behavioural analytics features like Heatmaps, Scroll Depth and Session Recording. These tools allow you to collect and analyse data without relying on cookies or resorting to data sampling.

  Those standout features can’t be found in GA4 or GA360. Google also doesn’t offer an on-premise solution.

  The one area where Matomo can’t compete with Google Analytics is in its tight integration with the Google ecosystem : Google Ads, Gemini and Firebase. 

  Key things to consider before switching to Matomo

  There are pros and cons to switching from GA4 (or even GA360) to Matomo. That’s because no software is perfect. There are always tradeoffs somewhere. With Matomo, there are a few things to consider before switching :

  • Learning curve. Matomo is a full-featured analytics platform with many advanced features (session replay, custom event tracking, etc.). That can overwhelm new users and take time to understand well enough to maximise the benefits.
  • Technical resources. Choosing a Matomo On-Premise solution requires technical resources, such as a server and skills.
  • Third-party integration. Matomo provides pre-built integration tools for about a hundred platforms. However, it’s open source, so technical resources are required. On the plus side, it does make it possible to add to the list of APIs and connectors.

  Head-to-head : GA4 vs GA360 vs Matomo

  It’s always helpful to look at how different products stack up in terms of features and capabilities :

  	GA4	GA360	Matomo
  Data ownership			✔
  Event-based data	✔	✔	✔
  Session-based data			✔
  Unsampled data			✔
  Real-time data	✔	✔	✔
  Heatmaps			✔
  Session recordings			✔
  A/B testing			✔
  Open source			✔
  On-premise hosting			✔
  Data privacy	Subject to Google’s data policies	Subject to Google’s data policies	GDPR, CCPA compliant ; full control over data storage
  Custom dimensions	Yes (limited in free version)	Yes (higher limits)	Yes (unlimited in self-hosted)
  Attribution models	Last click, data-driven	Last click, data-driven, advanced Google Ads integration	Last click, first click, linear, time decay, position-based, custom
  Data retention	Up to 14 months (free)	Up to 50 months	Unlimited (self-hosted)
  Integrations	Google Ads, Search Console, BigQuery (limited in free version)	Advanced integrations (Google Ads, BigQuery, Salesforce, etc.)	100+ integrations (Google Ads, WordPress, Shopify, etc.)
  BigQuery export	Free (limited to 1M events/day)	Free (unlimited)	Paid add-on (via plugin)
  Custom reports	Limited customisation	Advanced customisation	Fully customisable
  Scalability	Suitable for small to medium businesses	Designed for large enterprises	Scalable without limits (self-hosted or cloud)
  Ease of use	Simple, requires onboarding	Steeper learning curve	Flexible, setup-intensive.
  Pricing	Free	Premium (starts at $50,000/year)	Free open-source (self-hosted) ; Cloud starts at $29/month

  So, is Matomo the right solution for you ?

  That’d be a ‘yes’ if you want a Google Analytics alternative that ticks all these boxes :

  • Complies natively with privacy laws and regulations
  • Offers real-time data and custom event tracking
  • Enables a deeper understanding of user behaviour
  • Allows you to fine-tune user experiences
  • Provides full control over your customers’ data
  • Offers conversion funnels, session recordings and heatmaps
  • Has session replay to trace user interactions
  • Includes plenty of readily actionable insights

  Find out why millions of websites trust Matomo

  Matomo is an easy-to-use, all-in-one web analytics tool with advanced behavioural analytics functionality.

  It’ll also help you future-proof your business because it supports compliance with global privacy laws in 162 countries. With an ethical alternative like Matomo, you don’t need to risk your business or customers’ private data.

  It’s not just about avoiding fines. It’s also about building trust with your customers. That’s why you need a privacy-focused, ethical solution like Matomo. 

  See for yourself : download Matomo On-Premise today, or start your 21-day free trial of Matomo Cloud (no credit card required).

• What is a Cohort Report ? A Beginner’s Guide to Cohort Analysis

  3 janvier 2024, par Erin

  Handling your user data as a single mass of numbers is rarely conducive to figuring out meaningful patterns you can use to improve your marketing campaigns.

  A cohort report (or cohort analysis) can help you quickly break down that larger audience into sequential segments and contrast and compare based on various metrics. As such, it is a great tool for unlocking more granular trends and insights — for example, identifying patterns in engagement and conversions based on the date users first interacted with your site.

  In this guide, we explain the basics of the cohort report and the best way to set one up to get the most out of it.

  What is a cohort report ?

  In a cohort report, you divide a data set into groups based on certain criteria — typically a time-based cohort metric like first purchase date — and then analyse the data across those segments, looking for patterns.

  Date-based cohort analysis is the most common approach, often creating cohorts based on the day a user completed a particular action — signed up, purchased something or visited your website. Depending on the metric you choose to measure (like return visits), the cohort report might look something like this :

  

  Note that this is not a universal benchmark or anything of the sort. The above is a theoretical cohort analysis based on app users who downloaded the app, tracking and comparing the retention rates as the days go by. 

  The benchmarks will be drastically different depending on the metric you’re measuring and the basis for your cohorts. For example, if you’re measuring returning visitor rates among first-time visitors to your website, expect single-digit percentages even on the second day.

  Your industry will also greatly affect what you consider positive in a cohort report. For example, if you’re a subscription SaaS, you’d expect high continued usage rates over the first week. If you sell office supplies to companies, much less so.

  What is an example of a cohort ?

  As we just mentioned, a typical cohort analysis separates users or customers by the date they first interacted with your business — in this case, they downloaded your app. Within that larger analysis, the users who downloaded it on May 3 represent a single cohort.

  

  In this case, we’ve chosen behaviour and time — the app download day — to separate the user base into cohorts. That means every specific day denotes a specific cohort within the analysis.

  Diving deeper into an individual cohort may be a good idea for important holidays or promotional events like Black Friday.

  Of course, cohorts don’t have to be based on specific behaviour within certain periods. You can also create cohorts based on other dimensions :

  • Transactional data — revenue per user
  • Churn data — date of churn
  • Behavioural cohort — based on actions taken on your website, app or e-commerce store, like the number of sessions per user or specific product pages visited
  • Acquisition cohort — which channel referred the user or customer

  For more information on different cohort types, read our in-depth guide on cohort analysis.

  How to create a cohort report (and make sense of it)

  Matomo makes it easy to view and analyse different cohorts (without the privacy and legal implications of using Google Analytics).

  Here are a few different ways to set up a cohort report in Matomo, starting with our built-in cohorts report.

  Cohort reports

  With Matomo, cohort reports are automatically compiled based on the first visit date. The default metric is the percentage of returning visitors.

  

  Changing the settings allows you to create multiple variations of cohort analysis reports.

  Break down cohorts by different metrics

  The percentage of returning visits can be valuable if you’re trying to improve early engagement in a SaaS app onboarding process. But it’s far from your only option.

  You can also compare performance by conversion, revenue, bounce rate, actions per visit, average session duration or other metrics.

  

  Change the time and scope of your cohort analysis

  Splitting up cohorts by single days may be useless if you don’t have a high volume of users or visitors. If the average cohort size is only a few users, you won’t be able to identify reliable patterns. 

  Matomo lets you set any time period to create your cohort analysis report. Instead of the most recent days, you can create cohorts by week, month, year or custom date ranges. 

  

  Cohort sizes will depend on your customer base. Make sure each cohort is large enough to encapsulate all the customers in that cohort and not so small that you have insignificant cohorts of only a few customers. Choose a date range that gives you that without scaling it too far so you can’t identify any seasonal trends.

  Cohort analysis can be a great tool if you’ve recently changed your marketing, product offering or onboarding. Set the data range to weekly and look for any impact in conversions and revenue after the changes.

  Using the “compare to” feature, you can also do month-over-month, quarter-over-quarter or any custom date range comparisons. This approach can help you get a rough overview of your campaign’s long-term progress without doing any in-depth analysis.

  You can also use the same approach to compare different holiday seasons against each other.

  If you want to combine time cohorts with segmentation, you can run cohort reports for different subsets of visitors instead of all visitors. This can lead to actionable insights like adjusting weekend or specific seasonal promotions to improve conversion rates.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Easily create custom cohort reports beyond the time dimension

  If you want to split your audience into cohorts by focusing on something other than time, you will need to create a custom report and choose another dimension. In Matomo, you can choose from a wide range of cohort metrics, including referrers, e-commerce signals like viewed product or product category, form submissions and more.

  

  Then, you can create a simple table-based report with all the insights you need by choosing the metrics you want to see. For example, you could choose average visit duration, bounce rate and other usage metrics.

  

  If you want more revenue-focused insights, add metrics like conversions, add-to-cart and other e-commerce events.

  Custom reports make it easy to create cohort reports for almost any dimension. You can use any metric within demographic and behavioural analytics to create a cohort. (You can explore the complete list of our possible segmentation metrics.)

  We cover different types of custom reports (and ideas for specific marketing campaigns) in our guide on custom segmentation.

  Create your first cohort report and gain better insights into your visitors

  Cohort reports can help you identify trends and the impact of short-term marketing efforts like events and promotions.

  With Matomo cohort reports you have the power to create complex custom reports for various cohorts and segments. 

  If you’re looking for a powerful, easy-to-use web analytics solution that gives you 100% accurate data without compromising your users’ privacy, Matomo is a great fit. Get started with a 21-day free trial today. No credit card required. 

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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• VideoWriter Doesn't work using openCV, ubuntu, Qt

  25 janvier 2023, par underflow223

  My code :

  


  cv::VideoWriter(
  strFile.toStdString(),
  cv::VideoWriter::fourcc('m','p','4','v'),
  nfps,
  cv::Size(1920/nresize, 1080/nresize)
);


  


  Error message :

  


  [mpeg4_v4l2m2m @ 0x7f50a43c50] arm_release_ver of this libmali is 'g6p0-01eac0', rk_so_ver is '7'.
Could not find a valid device
[mpeg4_v4l2m2m @ 0x7f50a43c50] can't configure encoder


  


  If I use MJPG codec, it works fine thow.

  


  This is OPENCV configure info :

  


  -- General configuration for OpenCV 4.6.0 =====================================
--   Version control:               unknown
-- 
--   Extra modules:
--     Location (extra):            /home/firefly/Downloads/opencv_contrib-4.6.0/modules
--     Version control (extra):     unknown
-- 
--   Platform:
--     Timestamp:                   2023-01-19T02:11:26Z
--     Host:                        Linux 5.10.110 aarch64
--     CMake:                       3.16.3
--     CMake generator:             Unix Makefiles
--     CMake build tool:            /usr/bin/make
--     Configuration:               Release
-- 
--   CPU/HW features:
--     Baseline:                    NEON FP16
-- 
--   C/C++:
--     Built as dynamic libs?:      YES
--     C++ standard:                11
--     C++ Compiler:                /usr/bin/c++  (ver 9.4.0)
--     C++ flags (Release):         -fsigned-char -W -Wall -Wreturn-type -Wnon-virtual-dtor -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections    -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
--     C++ flags (Debug):           -fsigned-char -W -Wall -Wreturn-type -Wnon-virtual-dtor -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections    -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
--     C Compiler:                  /usr/bin/cc
--     C flags (Release):           -fsigned-char -W -Wall -Wreturn-type -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections    -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
--     C flags (Debug):             -fsigned-char -W -Wall -Wreturn-type -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections    -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
--     Linker flags (Release):      -Wl,--gc-sections -Wl,--as-needed -Wl,--no-undefined  
--     Linker flags (Debug):        -Wl,--gc-sections -Wl,--as-needed -Wl,--no-undefined  
--     ccache:                      NO
--     Precompiled headers:         NO
--     Extra dependencies:          dl m pthread rt
--     3rdparty dependencies:
-- 
--   OpenCV modules:
--     To be built:                 aruco barcode bgsegm bioinspired calib3d ccalib core datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot quality rapid reg rgbd saliency shape stereo stitching structured_light superres surface_matching text tracking ts video videoio videostab wechat_qrcode xfeatures2d ximgproc xobjdetect xphoto
--     Disabled:                    world
--     Disabled by dependency:      -
--     Unavailable:                 alphamat cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cvv hdf java julia matlab ovis python2 python3 sfm viz
--     Applications:                tests perf_tests apps
--     Documentation:               NO
--     Non-free algorithms:         NO
-- 
--   GUI:                           GTK3
--     GTK+:                        YES (ver 3.24.20)
--       GThread :                  YES (ver 2.64.6)
--       GtkGlExt:                  NO
--     VTK support:                 NO
-- 
--   Media I/O: 
--     ZLib:                        /usr/lib/aarch64-linux-gnu/libz.so (ver 1.2.11)
--     JPEG:                        /usr/lib/aarch64-linux-gnu/libjpeg.so (ver 80)
--     WEBP:                        build (ver encoder: 0x020f)
--     PNG:                         /usr/lib/aarch64-linux-gnu/libpng.so (ver 1.6.37)
--     TIFF:                        /usr/lib/aarch64-linux-gnu/libtiff.so (ver 42 / 4.1.0)
--     JPEG 2000:                   build (ver 2.4.0)
--     OpenEXR:                     build (ver 2.3.0)
--     HDR:                         YES
--     SUNRASTER:                   YES
--     PXM:                         YES
--     PFM:                         YES
-- 
--   Video I/O:
--     DC1394:                      YES (2.2.5)
--     FFMPEG:                      YES
--       avcodec:                   YES (58.54.100)
--       avformat:                  YES (58.29.100)
--       avutil:                    YES (56.31.100)
--       swscale:                   YES (5.5.100)
--       avresample:                YES (4.0.0)
--     GStreamer:                   YES (1.16.2)
--     v4l/v4l2:                    YES (linux/videodev2.h)
-- 
--   Parallel framework:            pthreads
-- 
--   Trace:                         YES (with Intel ITT)
-- 
--   Other third-party libraries:
--     Lapack:                      NO
--     Eigen:                       NO
--     Custom HAL:                  YES (carotene (ver 0.0.1))
--     Protobuf:                    build (3.19.1)
-- 
--   OpenCL:                        YES (no extra features)
--     Include path:                /home/firefly/Downloads/opencv-4.6.0/3rdparty/include/opencl/1.2
--     Link libraries:              Dynamic load
-- 
--   Python (for build):            /usr/bin/python2.7
-- 
--   Java:                          
--     ant:                         NO
--     JNI:                         NO
--     Java wrappers:               NO
--     Java tests:                  NO
-- 
============================================================================================


  


  ffmpeg info :

  


  ============================================================================================
ffmpeg
ffmpeg version 4.2.4-1ubuntu1.0firefly5 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)
  configuration: --prefix=/usr --extra-version=1ubuntu1.0firefly5 --toolchain=hardened --libdir=/usr/lib/aarch64-linux-gnu --incdir=/usr/include/aarch64-linux-gnu --arch=arm64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-libdrm --enable-librga --enable-rkmpp --enable-version3 --disable-libopenh264 --disable-vaapi --disable-vdpau --disable-decoder=h264_v4l2m2m --disable-decoder=vp8_v4l2m2m --disable-decoder=mpeg2_v4l2m2m --disable-decoder=mpeg4_v4l2m2m --enable-shared --disable-doc
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
Hyper fast Audio and Video encoder
usage: ffmpeg [options] [[infile options] -i infile]... {[outfile options] outfile}...
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