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• How to Use Web Analytics to Improve SEO

  5 janvier 2022, par erin — Analytics Tips

  Everyone wants their website to rank highly in Google — and that’s exactly why the world of SEO is so competitive.

  In order to succeed in such a crowded space, it’s essential to equip yourself with the right tools and processes to ensure your website is maximally optimised for search engines.

  If you’d like to improve your website’s SEO rankings, leveraging web analytics is one of the best places to start. Web analytics provides valuable insights to help you assess performance, user behaviour and optimisation opportunities.

  In this blog, we’ll cover :

  The basics of SEO and web analytics

  Before we discuss how to use web analytics for SEO, let’s start with a quick explanation of both.

  SEO (Search Engine Optimisation) encompasses a broad set of activities aimed at increasing a website’s position in search engine results pages (SERPs). When a user enters a query (e.g. ‘marketing agencies in Dallas’) in a search engine, the websites that appear near the top of the page are optimised for search engines and therefore ranking for that particular term. 

  Web analytics refers to the monitoring/assessment of metrics that track traffic sources and user behaviour on a website. This involves the use of a web analytics tool to collect, aggregate, organise and visualise website data so that meaningful conclusions can be drawn.

  The importance of website analytics for SEO

  SEO revolves around search engine algorithms – a set of rules that dictates a website’s ranking for a given search query (i.e. keyword). The algorithm takes numerous factors into account to determine a particular site’s SERP ranking. So, to achieve strong SEO, your website needs to exhibit qualities that the algorithm deems important. That’s where web analytics comes into play.

  Web analytics allows you to track key metrics and data points that affect how the algorithm ranks your website. For example, how much time do users spend on your site ? Which external links are referring traffic to your site ? How do your site’s Core Web Vitals stack up ? 

  Understanding this data will supply you with the insights needed to make positive adjustments, ultimately improving your website’s SEO. 

  How do you analyse a website for SEO ?

  The SEO analysis of a website needs to be focused on relevant data that’s applicable to search engine rankings. When conducting your website SEO analysis, here are some notable metrics and data fields to pay attention to :

  1. Bounce rate and dwell time

  These metrics denote how much time users spend on your website. If users frequently exit your site after only a few seconds, Google may view this as a negative indicator. To reduce bounce rate and increase dwell time, you should work towards making your site’s content more captivating and ensuring that there aren’t any technical issues with your site (e.g. pages taking too long to load or not optimised for mobile).

  

  2. Broken/dead links

  Perform a technical analysis to scan your website for faulty links. If your site contains broken links that lead to 404 pages, this can detract from your website’s SEO rankings. Redirect those links to a related page or remove them.

  

  Matomo’s Crawling Errors report can give you instant access to this technical information so you can resolve it before it begins to impact your ranking.

  3. Scroll depth

  Measuring scroll depth (how far users scroll down the page) can help you gauge the quality of your content — and this goes hand-in-hand with bounce rate and dwell time. To assess scroll depth, you can use a Tag Manager to track the specific scroll percentage on your site’s pages.

  4. Transitions

  Studying how users transition from page to page within your site can help you understand their behaviour more holistically. Which pages do they tend to gravitate towards ? Are there CTAs on your blog that aren’t driving many click-throughs ? Optimising user journeys will, in turn, elevate the overall user experience on your site.

  

  5. Internal site search

  You can use site search tracking and reporting to learn what your audience is looking for. If you notice a trend (e.g. the majority of searches are for pricing because your pricing page isn’t in the navigation menu), this can inform both site architecture and content planning.

  

  Ecommerce sites in particular should be monitoring branded queries, especially in regards to brand misspellings that could be causing users to bounce off the site.

  6. Segments

  Separating your visitors into distinct segments can produce granular insights that paint a more accurate picture.

  For example, perhaps you notice that your bounce rate is far higher on mobile, or with users from the UK. In both cases, this knowledge will provide clarity on where to focus your optimisation efforts (e.g. mobile responsiveness, UK-specific content/landing pages, etc.).

  

  7. Acquisition channels

  It’s crucial to analyse where your website traffic is coming from. Among other things, reviewing your acquisition metrics will reveal which external websites are referring the most traffic to your website. 

  Links from external sites (also known as backlinks) are one of the most important ranking factors because this tells Google that your site is reputable and credible. So, you may choose to cultivate a relationship with these sites (or similar sites) by offering guest blogging and other link building initiatives.

  

  In addition to the above, you should also be monitoring your Core Web Vitals — which leads us to our next section.

  What are Core Web Vitals and why are they important ?

  Core Web Vitals are a set of 3 primary metrics that reflect the general user experience of a website. These metrics are load time, interactivity and stability. 

  1. Load time (LCP) refers to the amount of time it takes for your website’s text and images to load.
  2. Interactivity (FID) refers to the amount of time it takes for user input areas (buttons, form fields, etc.) to become functional.
  3. Stability (CLS) refers to the visual/spatial integrity of your website. If text, images, and other elements tend to suddenly shift position when a user is viewing the site, this will hurt your CLS score.

  

  So, why are these Core Web Vitals metrics important for SEO ? Generally speaking, Google prioritises user experience — and Core Web Vitals affect users’ satisfaction with a website. Furthermore, Google has confirmed that Core Web Vitals are, indeed, a ranking factor.

  Matomo enables you to track metrics for Core Web Vitals which we refer to as SEO Web Vitals.

  How to measure and track keyword performance

  We can’t talk about SEO and analytics without touching on keywords. Keywords (the words/phrases that users type in a search engine) are arguably the most cardinal component of SEO. So, outside of website performance, it’s also necessary to track the keywords your website is ranking for. 

  Recall from above that SEO is all about ranking highly on SERPs for certain search queries (i.e. keywords). To assess your Search Engine Keyword Performance, you can use an analytics tool to view Keyword reports for your website. These reports will show you which keywords your site ranks for, the average SERP position your site achieves for each keyword, the amount of traffic you receive from each keyword, and more.

  

  Digging into your keyword performance can help you identify valuable keyword opportunities and improvement goals.

  For example, upon reviewing your highest-traffic keywords, you may choose to create more blog content around those keywords to bolster your success. Or, perhaps you notice that your average position for a high-intent keyword is quite low. In that case, you could implement a targeted link building campaign to help boost your ranking for that keyword. 

  Final thoughts

  In this article, we’ve discussed the benefits of web analytics — particularly in regards to SEO. When it comes to selecting a web analytics tool, Google Analytics is by far the most popular choice. But that doesn’t make it the best.

  At Matomo, we’re committed to providing a superior alternative to Google Analytics. Matomo is a powerful, open-source web analytics platform that gives you 100% data ownership — protecting both your data and your customers’ privacy.

  Try our live demo or start a free 21-day trial now – no credit card required.

• 5 Key Benefits of Using a Tag Manager

  12 décembre 2021, par erin — Analytics Tips, Marketing

  Websites today have become very complex to manage, and as you continue to look for ways to optimise your website, you’ll want to consider using a Tag Manager. 

  A Tag Manager will help your marketing team seamlessly track how your visitors are engaging with your website’s elements. Without a Tag Manager, you are missing out on business-altering insights.

  In this blog, we’ll cover :

  Tag Manager overview 

  A Tag Manager (AKA Tag Management System or TMS) is a centralised system for implementing, managing and tracking events. A tag is just another word for a piece of code on a website that tracks a specific event. 

  An example of a tag tracking code might be Facebook pixels, ad conversions and other website activities such as signing up to a newsletter or PDF download. 

  Triggers are the actual actions that website visitors take that activate the tag. Examples of triggers are things like : 

  • A thank you page view to show that a visitor has completed a conversion action
  • Clicking a download or sign up button 
  • Scroll depth or how far down users are scrolling on your webpage 

  Each of these will give you insights into how your website is performing and how your users are engaging with your content. Going back to the scroll depth trigger example, this would be particularly helpful for validating bounce rate and finding out where users are dropping off on a page. Discover other ways to take advantage of tags and event tracking. 

  

  5 key benefits of a Tag Manager

  1. Removes the risks of website downtime 

  Tags are powerful for in-depth web analytics. However, tagging opens up the potential for non-technical team members to break the front-end of your website in a couple of clicks. 

  A Tag Manager reduces that risk. For example, Matomo Tag Manager lets you preview tags to see if they are firing before pushing them live. You can also give specific users restricted access so you can approve any tagging before it goes live. 

  Tag Managers protect the functionality of your website and ensure that there is no downtime.

  2. Your website will load faster 

  When it comes to the success of your website, page speed is one of the most important factors. 

  Each time you add a tag to your site, you run the risk of slowing down the page speed. This can quickly build up to a poor performing site and frustrate your visitors.

  You can’t track tags if visitors won’t even stay long enough for your site to load. In fact, 1 in 4 visitors would abandon a website that takes more than 4 seconds to load. According to Deloitte, just a 0.1 second difference in loading speed can affect every step of your customer journey. 

  A Tag Manager, on the other hand, is a lightweight option only requiring one single tag. Using a Tag Manager to track events can make all the difference to your website’s performance and user experience.

  3. Greater efficiency for marketing

  Time is critical in marketing. The longer it takes for a campaign to launch, the greater the chances are that you’re missing out on sales opportunities.

  Waiting for the IT team to tag a thank you page before setting an ad live is inefficient and impacts your bottom line.

  Equipping marketing with a Tag Manager means that they’ll be able to launch campaigns faster and more effectively.

  Check out our Marketer’s Guide to Successful Website Event Tracking for more.

  4. Control all of your tracking and marketing tags in one place 

  Keeping track of what tags are on your site and where they’re located is a complicated task if you aren’t using a Tag Manager. Unmanaged tags can quickly pile up and result in errors with your analytics, like counting conversions twice. 

  Using a Tag Manager to centralise your tags in one easy to manage place reduces the chances of human errors. Instead, your team will be able to quickly see what tags are already in place so they aren’t doubling up on tracking.

  5. Reduce work for the IT team 

  Let’s face it, the IT team has more critical tasks at hand than adding tags to the website. Freeing up your IT team to focus on higher priority tasks should always be a goal.

  Tagging, while crucial for marketing, has the potential to create a lot of extra work for your website developers. Inserting code for each individual tag is time-consuming and means you aren’t collecting data in the meantime.

  Rather than overloading your IT team, empower your marketing team with the ability to add tags with a few clicks. 

  How to choose a Tag Management System

  There are many tools to choose from and the default option tends to be Google Tag Manager (GTM). But before you implement GTM or any other Tag Management Solution, we highly recommend asking these questions :

  1. What are my goals for a Tag Manager ? Before purchasing a Tag Manager, or any tool for that matter, understanding your goals upfront is best practice.
  2. Does the solution offer Tag Manager training resources ? If online Tag Manager training and educational resources are available for the tool, then you’ll be able to hit the ground running and start to see an ROI instantly.
  3. Can I get online support ? In case you need any help with the tool, having access to online support is a big bonus. 
  4. Is it compliant with privacy regulations ? If your business is already compliant, in the process of becoming compliant or future-proofing your tech stack for looming privacy regulations, then researching this is crucial. 
  5. How much does it cost ? If it’s “free”, find out how and why. In most cases, free solutions are just vehicles for collecting data to advertise to your users. 
  6. What do others think about the Tag Manager ? Check out reviews on sites like Capterra or G2 to find out how other businesses rate the tool. 

  Google Tag Manager alternative

  As privacy becomes a greater concern globally for end-users and governments, many businesses are looking for alternatives to the world’s largest advertising company – Google.

  Matomo Tag Manager is more than a Google Tag Manager alternative. With Matomo Tag Manager, you get a GDPR, HIPAA, CCPA and PECR compliant, open source Tag Manager and your data is 100% yours to own.

  Plus, with Matomo Tag Manager you only need one single tracking code for all of your website and tag analytics. No matter what you are tracking (scrolls, clicks, downloads, Heatmaps, visits, etc.), you will only ever need one piece of code on your website and one tool to manage it all. 

  The takeaway 

  Tagging is powerful but can quickly become complicated, risky and time-consuming. Tag Managers reduce these obstacles allowing you to set tags and triggers effortlessly. It empowers marketing teams, streamlines processes and removes the reliance on IT.

  Ready to try Matomo Tag Manager ? Start your 21-day free trial now – no credit card required. 

• opencv does not find ffmpeg functions during compilation (make)

  17 avril 2022, par titicplusplus

  I am currently trying to compile OpenCV with CUDA.
So I downloaded opencv 4.5.5 and opencv_contrib and followed this tutorial : https://gist.github.com/raulqf/f42c718a658cddc16f9df07ecc627be7

  


  cd opencv-4.5.5/
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_TBB=ON \
-D ENABLE_FAST_MATH=1 \
-D CUDA_FAST_MATH=1 \
-D WITH_CUBLAS=1 \
-D WITH_CUDA=ON \
-D BUILD_opencv_cudacodec=OFF \
-D WITH_CUDNN=OFF \
-D OPENCV_DNN_CUDA=OFF \
-D CUDA_ARCH_BIN=7.5 \
-D WITH_V4L=ON \
-D WITH_QT=ON \
-D WITH_OPENGL=ON \
-D WITH_GSTREAMER=ON \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D OPENCV_PC_FILE_NAME=opencv.pc \
-D OPENCV_ENABLE_NONFREE=ON \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-D INSTALL_C_EXAMPLES=OFF \
-D BUILD_EXAMPLES=OFF \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.5.5/modules ../


  


  The cmake command generated these lines :

  


  -- General configuration for OpenCV 4.5.5 =====================================
--   Version control:               unknown
-- 
--   Extra modules:
--     Location (extra):            /mnt/704E048C4E044D72/build/opencv/opencv_contrib-4.5.5/modules
--     Version control (extra):     unknown
-- 
--   Platform:
--     Timestamp:                   2022-04-17T16:01:44Z
--     Host:                        Linux 5.4.0-107-lowlatency x86_64
--     CMake:                       3.16.3
--     CMake generator:             Unix Makefiles
--     CMake build tool:            /usr/bin/make
--     Configuration:               RELEASE
-- 
--   CPU/HW features:
--     Baseline:                    SSE SSE2 SSE3
--       requested:                 SSE3
--     Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2 AVX512_SKX
--       requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
--       SSE4_1 (18 files):         + SSSE3 SSE4_1
--       SSE4_2 (2 files):          + SSSE3 SSE4_1 POPCNT SSE4_2
--       FP16 (1 files):            + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
--       AVX (5 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
--       AVX2 (33 files):           + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2
--       AVX512_SKX (8 files):      + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2 AVX_512F AVX512_COMMON AVX512_SKX
-- 
--   C/C++:
--     Built as dynamic libs?:      YES
--     C++ standard:                11
--     C++ Compiler:                /usr/bin/c++  (ver 8.4.0)
--     C++ flags (Release):         -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-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 -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
--     C++ flags (Debug):           -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-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 -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
--     C Compiler:                  /usr/bin/cc
--     C flags (Release):           -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-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 -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
--     C flags (Debug):             -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-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 -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
--     Linker flags (Release):      -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed  
--     Linker flags (Debug):        -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed  
--     ccache:                      NO
--     Precompiled headers:         NO
--     Extra dependencies:          m pthread cudart_static dl rt nppc nppial nppicc nppicom nppidei nppif nppig nppim nppist nppisu nppitc npps cublas cufft -L/usr/lib/x86_64-linux-gnu
--     3rdparty dependencies:
-- 
--   OpenCV modules:
--     To be built:                 alphamat aruco barcode bgsegm bioinspired calib3d ccalib core cudaarithm cudabgsegm cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cvv datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hdf hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot python2 python3 quality rapid reg rgbd saliency sfm shape stereo stitching structured_light superres surface_matching text tracking ts video videoio videostab wechat_qrcode xfeatures2d ximgproc xobjdetect xphoto
--     Disabled:                    cudacodec world
--     Disabled by dependency:      -
--     Unavailable:                 java julia matlab ovis viz
--     Applications:                tests perf_tests apps
--     Documentation:               NO
--     Non-free algorithms:         YES
-- 
--   GUI:                           QT5
--     QT:                          YES (ver 5.12.8 )
--       QT OpenGL support:         YES (Qt5::OpenGL 5.12.8)
--     GTK+:                        YES (ver 3.24.20)
--       GThread :                  YES (ver 2.64.6)
--       GtkGlExt:                  NO
--     OpenGL support:              YES (/usr/lib/x86_64-linux-gnu/libGL.so /usr/lib/x86_64-linux-gnu/libGLU.so)
--     VTK support:                 NO
-- 
--   Media I/O: 
--     ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.11)
--     JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver 80)
--     WEBP:                        build (ver encoder: 0x020f)
--     PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.6.37)
--     TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 / 4.1.0)
--     JPEG 2000:                   build (ver 2.4.0)
--     OpenEXR:                     /usr/lib/x86_64-linux-gnu/libImath.so /usr/lib/x86_64-linux-gnu/libIlmImf.so /usr/lib/x86_64-linux-gnu/libIex.so /usr/lib/x86_64-linux-gnu/libHalf.so /usr/lib/x86_64-linux-gnu/libIlmThread.so (ver 2_3)
--     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:            TBB (ver 2020.1 interface 11101)
-- 
--   Trace:                         YES (with Intel ITT)
-- 
--   Other third-party libraries:
--     Intel IPP:                   2020.0.0 Gold [2020.0.0]
--            at:                   /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/build/3rdparty/ippicv/ippicv_lnx/icv
--     Intel IPP IW:                sources (2020.0.0)
--               at:                /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/build/3rdparty/ippicv/ippicv_lnx/iw
--     VA:                          NO
--     Lapack:                      NO
--     Eigen:                       YES (ver 3.3.7)
--     Custom HAL:                  NO
--     Protobuf:                    build (3.19.1)
-- 
--   NVIDIA CUDA:                   YES (ver 10.1, CUFFT CUBLAS FAST_MATH)
--     NVIDIA GPU arch:             75
--     NVIDIA PTX archs:
-- 
--   OpenCL:                        YES (no extra features)
--     Include path:                /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/3rdparty/include/opencl/1.2
--     Link libraries:              Dynamic load
-- 
--   Python 2:
--     Interpreter:                 /usr/bin/python2.7 (ver 2.7.18)
--     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython2.7.so (ver 2.7.18)
--     numpy:                       /usr/lib/python2.7/dist-packages/numpy/core/include (ver 1.16.5)
--     install path:                lib/python2.7/dist-packages/cv2/python-2.7
-- 
--   Python 3:
--     Interpreter:                 /usr/bin/python3 (ver 3.8.10)
--     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.8.so (ver 3.8.10)
--     numpy:                       /home/famillevincent/.local/lib/python3.8/site-packages/numpy/core/include (ver 1.22.3)
--     install path:                lib/python3.8/site-packages/cv2/python-3.8
-- 
--   Python (for build):            /usr/bin/python2.7
-- 
--   Java:                          
--     ant:                         NO
--     JNI:                         /usr/lib/jvm/default-java/include /usr/lib/jvm/default-java/include/linux /usr/lib/jvm/default-java/include
--     Java wrappers:               NO
--     Java tests:                  NO
-- 
--   Install to:                    /usr/local
-- -----------------------------------------------------------------
-- 
-- Configuring done
-- Generating done


  


  CMake have detected ffmpeg libraries, but when I run make -j8. I have this error :

  


  [ 39%] Building CXX object apps/interactive-calibration/CMakeFiles/opencv_interactive-calibration.dir/calibPipeline.cpp.o
Scanning dependencies of target opencv_cudafilters
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_transfer_data »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_get_hw_config »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_get_hwframe_constraints »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_send_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_get_buffer »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_ctx_create_derived »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_ctx_create »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_alloc »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_receive_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_send_frame »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_parameters_copy »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_packet_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_find_type_by_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_init »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_alloc »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_receive_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_codec_iterate »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_init »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_get_type_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_constraints_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_receive_frame »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_get_by_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_get_hw_frames_parameters »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_send_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_create_derived »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_packet_alloc »
collect2: error: ld returned 1 exit status
make[2]: *** [apps/visualisation/CMakeFiles/opencv_visualisation.dir/build.make:92 : bin/opencv_visualisation] Erreur 1
make[1]: *** [CMakeFiles/Makefile2:11412 : apps/visualisation/CMakeFiles/opencv_visualisation.dir/all] Erreur 2
make[1]: *** Attente des tâches non terminées....
[ 39%] Building CXX object apps/interactive-calibration/CMakeFiles/opencv_interactive-calibration.dir/frameProcessor.cpp.o


  


  So what can I do to compile opencv with cuda and ffmpeg ?
  
Thank you in advance for your answers.

  


  I use Ubuntu 20.04 with g++8