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• trying to make OpenCV 3.2.0 work with virtualenv

  24 juillet 2017, par lollercoaster

  I’m on Ubuntu 16.04 with Python 2.7 and virtualenv & virtualenvwrapper.

  By following this guide I managed to get the following script working with my system Python2.7 which has cv2 globally installed.

  I used this script to install it :

  ######################################
  
  # INSTALL OPENCV ON UBUNTU OR DEBIAN #
  
  ######################################
  
  
  
  # |         THIS SCRIPT IS TESTED CORRECTLY ON         |
  
  # |----------------------------------------------------|
  
  # | OS             | OpenCV       | Test | Last test   |
  
  # |----------------|--------------|------|-------------|
  
  # | Ubuntu 16.04.2 | OpenCV 3.2.0 | OK   | 20 May 2017 |
  
  # | Debian 8.8     | OpenCV 3.2.0 | OK   | 20 May 2017 |
  
  # | Debian 9.0     | OpenCV 3.2.0 | OK   | 25 Jun 2017 |
  
  
  
  # 1. KEEP UBUNTU OR DEBIAN UP TO DATE
  
  
  
  sudo apt-get -y update
  
  sudo apt-get -y upgrade
  
  sudo apt-get -y dist-upgrade
  
  sudo apt-get -y autoremove
  
  
  
  
  
  # 2. INSTALL THE DEPENDENCIES
  
  
  
  # Build tools:
  
  sudo apt-get install -y build-essential cmake
  
  
  
  # GUI (if you want to use GTK instead of Qt, replace 'qt5-default' with 'libgtkglext1-dev' and remove '-DWITH_QT=ON' option in CMake):
  
  sudo apt-get install -y qt5-default libvtk6-dev
  
  
  
  # Media I/O:
  
  sudo apt-get install -y zlib1g-dev libjpeg-dev libwebp-dev libpng-dev libtiff5-dev libjasper-dev libopenexr-dev libgdal-dev
  
  
  
  # Video I/O:
  
  sudo apt-get install -y libdc1394-22-dev libavcodec-dev libavformat-dev libswscale-dev libtheora-dev libvorbis-dev libxvidcore-dev libx264-dev yasm libopencore-amrnb-dev libopencore-amrwb-dev libv4l-dev libxine2-dev
  
  
  
  # Parallelism and linear algebra libraries:
  
  sudo apt-get install -y libtbb-dev libeigen3-dev
  
  
  
  # Python:
  
  sudo apt-get install -y python-dev python-tk python-numpy python3-dev python3-tk python3-numpy
  
  
  
  # Documentation:
  
  sudo apt-get install -y doxygen
  
  
  
  # UI stuff
  
  sudo apt-get install libgtk-3-dev libatlas-base-dev gfortran
  
  
  
  
  
  # 3. INSTALL THE LIBRARY (YOU CAN CHANGE '3.2.0' FOR THE LAST STABLE VERSION)
  
  sudo apt-get install -y unzip wget
  
  
  
  # opencv contrib
  
  wget https://github.com/opencv/opencv_contrib/archive/3.2.0.zip -O opencv_contrib-3.2.0.zip
  
  unzip opencv_contrib-3.2.0.zip
  
  rm opencv_contrib-3.2.0.zip
  
  
  
  # opencv
  
  wget https://github.com/opencv/opencv/archive/3.2.0.zip
  
  unzip 3.2.0.zip
  
  rm 3.2.0.zip
  
  mv opencv-3.2.0 OpenCV-3.2.0
  
  cd OpenCV-3.2.0
  
  
  
  mkdir build
  
  cd build
  
  cmake -D WITH_QT=ON \
  
      -D WITH_OPENGL=ON \
  
      -D FORCE_VTK=ON \
  
      -D WITH_TBB=ON \
  
      -D WITH_GDAL=ON \
  
      -D WITH_XINE=ON \
  
      -D BUILD_EXAMPLES=ON \
  
      -D INSTALL_PYTHON_EXAMPLES=ON \
  
      -D ENABLE_PRECOMPILED_HEADERS=OFF \
  
      -D BUILD_NEW_PYTHON_SUPPORT=ON \
  
      ..
  
  
  
  make -j4
  
  sudo make install
  
  sudo ldconfig
  
  
  
  
  
  # 4. EXECUTE SOME OPENCV EXAMPLES AND COMPILE A DEMONSTRATION
  
  
  
  # To complete this step, please visit 'http://milq.github.io/install-opencv-ubuntu-debian'.

  The following script below works great with that system-wide installation :

  import cv2
  
  
  
  img = cv2.imread('some_img.jpg')

  Though this one doesn’t - even the system Python can’t read videos for some reason...

  import cv2
  
  
  
  video_capture = cv2.VideoCapture(0)
  
  ret, frame = video_capture.read()
  
  print ret  # always False

  but I want it to work with my virtualenv. So I recompiled OpenCV with :

  cmake -D WITH_QT=ON \
  
      -D WITH_OPENGL=ON \
  
      -D FORCE_VTK=ON \
  
      -D WITH_TBB=ON \
  
      -D WITH_GDAL=ON \
  
      -D WITH_XINE=ON \
  
      -D BUILD_EXAMPLES=ON \
  
      -D INSTALL_PYTHON_EXAMPLES=ON \
  
      -D ENABLE_PRECOMPILED_HEADERS=OFF \
  
      -D BUILD_NEW_PYTHON_SUPPORT=ON \
  
      -D OPENCV_EXTRA_MODULES_PATH=/home/me/code/myproject/opencv_contrib-3.2.0/modules \
  
      -D PYTHON_EXECUTABLE=~/.envs/myenv/bin/python \
  
      ..
  
  
  
  make -j4
  
  sudo make install
  
  sudo ldconfig

  Here’s the CMake log :

  -- Found VTK ver. 6.2.0 (usefile: /usr/lib/cmake/vtk-6.2/UseVTK.cmake)
  
  -- Caffe:   NO
  
  -- Protobuf:   YES
  
  -- Glog:   NO
  
  -- freetype2:   YES
  
  -- harfbuzz:    YES
  
  -- Module opencv_sfm disabled because the following dependencies are not found: Glog/Gflags
  
  -- freetype2:   YES
  
  -- harfbuzz:    YES
  
  -- Checking for modules 'tesseract;lept'
  
  --   No package 'tesseract' found
  
  --   No package 'lept' found
  
  -- Tesseract:   NO
  
  -- Check contents of vgg_generated_48.i ...
  
  -- Check contents of vgg_generated_64.i ...
  
  -- Check contents of vgg_generated_80.i ...
  
  -- Check contents of vgg_generated_120.i ...
  
  -- Check contents of boostdesc_bgm.i ...
  
  -- Check contents of boostdesc_bgm_bi.i ...
  
  -- Check contents of boostdesc_bgm_hd.i ...
  
  -- Check contents of boostdesc_binboost_064.i ...
  
  -- Check contents of boostdesc_binboost_128.i ...
  
  -- Check contents of boostdesc_binboost_256.i ...
  
  -- Check contents of boostdesc_lbgm.i ...
  
  -- 
  
  -- General configuration for OpenCV 3.2.0 =====================================
  
  --   Version control:               817bd7b-dirty
  
  -- 
  
  --   Extra modules:
  
  --     Location (extra):            /home/me/code/myproject/opencv_contrib-3.2.0/modules
  
  --     Version control (extra):     817bd7b-dirty
  
  -- 
  
  --   Platform:
  
  --     Timestamp:                   2017-07-20T18:25:26Z
  
  --     Host:                        Linux 4.8.0-58-generic x86_64
  
  --     CMake:                       3.5.1
  
  --     CMake generator:             Unix Makefiles
  
  --     CMake build tool:            /usr/bin/make
  
  --     Configuration:               Release
  
  -- 
  
  --   C/C++:
  
  --     Built as dynamic libs?:      YES
  
  --     C++ Compiler:                /usr/bin/c++  (ver 5.4.0)
  
  --     C++ flags (Release):         -fsigned-char -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 -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
  
  --     C++ flags (Debug):           -fsigned-char -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 -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
  
  --     C Compiler:                  /usr/bin/cc
  
  --     C flags (Release):           -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
  
  --     C flags (Debug):             -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
  
  --     Linker flags (Release):
  
  --     Linker flags (Debug):
  
  --     ccache:                      NO
  
  --     Precompiled headers:         NO
  
  --     Extra dependencies:          Qt5::Test Qt5::Concurrent Qt5::OpenGL /usr/lib/x86_64-linux-gnu/libwebp.so /usr/lib/x86_64-linux-gnu/libjasper.so /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 /usr/lib/libgdal.so dc1394 xine avcodec-ffmpeg avformat-ffmpeg avutil-ffmpeg swscale-ffmpeg Qt5::Core Qt5::Gui Qt5::Widgets /usr/lib/x86_64-linux-gnu/hdf5/serial/lib/libhdf5.so /usr/lib/x86_64-linux-gnu/libpthread.so /usr/lib/x86_64-linux-gnu/libsz.so /usr/lib/x86_64-linux-gnu/libdl.so /usr/lib/x86_64-linux-gnu/libm.so vtkRenderingOpenGL vtkImagingHybrid vtkIOImage vtkCommonDataModel vtkCommonMath vtkCommonCore vtksys vtkCommonMisc vtkCommonSystem vtkCommonTransforms vtkCommonExecutionModel vtkDICOMParser vtkIOCore /usr/lib/x86_64-linux-gnu/libz.so vtkmetaio /usr/lib/x86_64-linux-gnu/libjpeg.so /usr/lib/x86_64-linux-gnu/libpng.so /usr/lib/x86_64-linux-gnu/libtiff.so vtkImagingCore vtkRenderingCore vtkCommonColor vtkFiltersExtraction vtkFiltersCore vtkFiltersGeneral vtkCommonComputationalGeometry vtkFiltersStatistics vtkImagingFourier vtkalglib vtkFiltersGeometry vtkFiltersSources vtkInteractionStyle vtkRenderingLOD vtkFiltersModeling vtkIOPLY vtkIOGeometry /usr/lib/x86_64-linux-gnu/libjsoncpp.so vtkFiltersTexture vtkRenderingFreeType /usr/lib/x86_64-linux-gnu/libfreetype.so vtkftgl vtkIOExport vtkRenderingAnnotation vtkImagingColor vtkRenderingContext2D vtkRenderingGL2PS vtkRenderingContextOpenGL /usr/lib/libgl2ps.so vtkRenderingLabel dl m pthread rt /usr/lib/x86_64-linux-gnu/libGLU.so /usr/lib/x86_64-linux-gnu/libGL.so tbb
  
  --     3rdparty dependencies:       libprotobuf
  
  -- 
  
  --   OpenCV modules:
  
  --     To be built:                 core flann hdf imgproc ml photo reg surface_matching video viz dnn freetype fuzzy imgcodecs shape videoio highgui objdetect plot superres ts xobjdetect xphoto bgsegm bioinspired dpm face features2d line_descriptor saliency text calib3d ccalib cvv datasets rgbd stereo tracking videostab xfeatures2d ximgproc aruco optflow phase_unwrapping stitching structured_light java python2 python3
  
  --     Disabled:                    world contrib_world
  
  --     Disabled by dependency:      -
  
  --     Unavailable:                 cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cnn_3dobj matlab sfm
  
  -- 
  
  --   GUI: 
  
  --     QT 5.x:                      YES (ver 5.5.1)
  
  --     QT OpenGL support:           YES (Qt5::OpenGL 5.5.1)
  
  --     OpenGL support:              YES (/usr/lib/x86_64-linux-gnu/libGLU.so /usr/lib/x86_64-linux-gnu/libGL.so)
  
  --     VTK support:                 YES (ver 6.2.0)
  
  -- 
  
  --   Media I/O: 
  
  --     ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.8)
  
  --     JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver )
  
  --     WEBP:                        /usr/lib/x86_64-linux-gnu/libwebp.so (ver encoder: 0x0202)
  
  --     PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.2.54)
  
  --     TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 - 4.0.6)
  
  --     JPEG 2000:                   /usr/lib/x86_64-linux-gnu/libjasper.so (ver 1.900.1)
  
  --     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 2.2.0)
  
  --     GDAL:                        /usr/lib/libgdal.so
  
  --     GDCM:                        NO
  
  -- 
  
  --   Video I/O:
  
  --     DC1394 1.x:                  NO
  
  --     DC1394 2.x:                  YES (ver 2.2.4)
  
  --     FFMPEG:                      YES
  
  --       avcodec:                   YES (ver 56.60.100)
  
  --       avformat:                  YES (ver 56.40.101)
  
  --       avutil:                    YES (ver 54.31.100)
  
  --       swscale:                   YES (ver 3.1.101)
  
  --       avresample:                NO
  
  --     GStreamer:                   NO
  
  --     OpenNI:                      NO
  
  --     OpenNI PrimeSensor Modules:  NO
  
  --     OpenNI2:                     NO
  
  --     PvAPI:                       NO
  
  --     GigEVisionSDK:               NO
  
  --     Aravis SDK:                  NO
  
  --     UniCap:                      NO
  
  --     UniCap ucil:                 NO
  
  --     V4L/V4L2:                    NO/YES
  
  --     XIMEA:                       NO
  
  --     Xine:                        YES (ver 1.2.6)
  
  --     gPhoto2:                     NO
  
  -- 
  
  --   Parallel framework:            TBB (ver 4.4 interface 9002)
  
  -- 
  
  --   Other third-party libraries:
  
  --     Use IPP:                     9.0.1 [9.0.1]
  
  --          at:                     /home/me/code/myproject/OpenCV-3.2.0/build/3rdparty/ippicv/ippicv_lnx
  
  --     Use IPP Async:               NO
  
  --     Use VA:                      NO
  
  --     Use Intel VA-API/OpenCL:     NO
  
  --     Use Lapack:                  NO
  
  --     Use Eigen:                   YES (ver 3.2.92)
  
  --     Use Cuda:                    NO
  
  --     Use OpenCL:                  YES
  
  --     Use OpenVX:                  NO
  
  --     Use custom HAL:              NO
  
  -- 
  
  --   OpenCL:                        <dynamic loading="loading" of="of" opencl="opencl" library="library">
  
  --     Include path:                /home/me/code/myproject/OpenCV-3.2.0/3rdparty/include/opencl/1.2
  
  --     Use AMDFFT:                  NO
  
  --     Use AMDBLAS:                 NO
  
  -- 
  
  --   Python 2:
  
  --     Interpreter:                 /home/me/.envs/myenv/bin/python (ver 2.7.12)
  
  --     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython2.7.so (ver 2.7.12)
  
  --     numpy:                       /home/me/.envs/myenv/local/lib/python2.7/site-packages/numpy/core/include (ver 1.13.1)
  
  --     packages path:               lib/python2.7/site-packages
  
  -- 
  
  --   Python 3:
  
  --     Interpreter:                 /usr/bin/python3 (ver 3.5.2)
  
  --     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.5m.so (ver 3.5.2)
  
  --     numpy:                       /usr/lib/python3/dist-packages/numpy/core/include (ver 1.11.0)
  
  --     packages path:               lib/python3.5/dist-packages
  
  -- 
  
  --   Python (for build):            /home/me/.envs/myenv/bin/python
  
  -- 
  
  --   Java:
  
  --     ant:                         /usr/bin/ant (ver 1.9.6)
  
  --     JNI:                         /usr/lib/jvm/default-java/include /usr/lib/jvm/default-java/include/linux /usr/lib/jvm/default-java/include
  
  --     Java wrappers:               YES
  
  --     Java tests:                  YES
  
  -- 
  
  --   Matlab:                        Matlab not found or implicitly disabled
  
  -- 
  
  --   Documentation:
  
  --     Doxygen:                     /usr/bin/doxygen (ver 1.8.11)
  
  -- 
  
  --   Tests and samples:
  
  --     Tests:                       YES
  
  --     Performance tests:           YES
  
  --     C/C++ Examples:              YES
  
  -- 
  
  --   Install path:                  /usr/local
  
  -- 
  
  --   cvconfig.h is in:              /home/me/code/myproject/OpenCV-3.2.0/build
  
  -- -----------------------------------------------------------------
  
  -- 
  
  </dynamic>

  Unfortunately, while this works and I can import cv2 in the shell, it cannot read video using the above script, probably due to incorrect compilation or linking of ffmpeg ? The confusing part is the system-wide installation of OpenCV works fine, even without ffmpeg installed !

  What am I doing wrong ? How can I get OpenCV working with a virtualenv ?

  ====

  EDIT : Running the C++ video writing example has this result :

  $ cd /home/me/code/myproject/OpenCV-3.2.0/build/bin
  
  $ ./cpp-tutorial-video-write ../../samples/data/vtest.avi R Y
  
  ------------------------------------------------------------------------------
  
  This program shows how to write video files.
  
  You can extract the R or G or B color channel of the input video.
  
  Usage:
  
  ./video-write  [ R | G | B] [Y | N]
  
  ------------------------------------------------------------------------------
  
  
  
  OpenCV: FFMPEG: tag 0xffffffff/'����' is not found (format 'avi / AVI (Audio Video Interleaved)')'
  
  
  
  (cpp-tutorial-video-write:19523): GStreamer-CRITICAL **: gst_element_make_from_uri: assertion 'gst_uri_is_valid (uri)' failed
  
  OpenCV Error: Unsupported format or combination of formats (Gstreamer Opencv backend does not support this codec.) in CvVideoWriter_GStreamer::open, file /home/me/code/myproject/OpenCV-3.2.0/modules/videoio/src/cap_gstreamer.cpp, line 1388
  
  VIDEOIO(cvCreateVideoWriter_GStreamer(filename, fourcc, fps, frameSize, is_color)): raised OpenCV exception:
  
  
  
  /home/me/code/myproject/OpenCV-3.2.0/modules/videoio/src/cap_gstreamer.cpp:1388: error: (-210) Gstreamer Opencv backend does not support this codec. in function CvVideoWriter_GStreamer::open
  
  
  
  Could not open the output video for write: ../../samples/data/vtest.avi

  And the opencv_test_videoio unit test reports the following : https://pastebin.com/q4mf224Q

  However, running the c++ video starter example DOES work, with the following command and output, I can see the webcam working and streaming video in the highgui interface :

  $ ./cpp-example-videocapture_starter 0
  
  VIDEOIO ERROR: V4L: device 0: Unable to query number of channels
  
  (ERROR)icvOpenAVI_XINE(): Unable to initialize video driver.
  
  GStreamer: Error opening bin: no element "0"
  
  press space to save a picture. q or esc to quit
  
  init done
  
  opengl support available

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  Matomo Analytics for WordPress is an ethical alternative to Google Analytics for WordPress

  If you’re more interested in a privacy-respecting, GDPR compliant alternative, there’s now a new option on the WP plugins directory : Matomo Analytics – Ethical Stats. Powerful Insights. 

  It’s free and can be considered the #1 ethical alternative to Google Analytics in terms of features and capabilities. Why is it important to choose a web analytics platform that respects privacy ?

  

  Risk facing fines for non-GDPR compliance and privacy/data breaches

  In Europe there’s an overarching privacy law called GDPR which provides better privacy protection for EU citizens on the web. 

  Websites need to be GDPR compliant and follow rules governing how personal data is used or risk facing fines up to 4% of their yearly revenue for data/privacy breaches or non-compliance. Even if your website is based outside of Europe. If you have visitors from Europe, you can still be liable.

  

  In the US, there isn’t one main privacy law, there hundreds on both the federal and state levels to protect the personal data (or personally identifiable information) of US residents – like the California Consumer Privacy Act (CCPA). There are also industry-specific statutes related to data privacy like HIPAA.

  To protect your website from coming under fire for privacy breaches, best practise is to find platforms that are privacy and GDPR compliant by design. 

  When you own your own data – as with the case of Matomo – you have control over where data is stored, what you’re doing with it, and can better protect the privacy of your visitors.

  At this point you may be asking, “what’s the point of an analytics platform if you have to follow all these rules ?”

  The importance of analytics for your WordPress site

  • Figuring out how your audience behaves to increase conversions
  • Setting, tracking and measuring conversion goals
  • Being able to find insights to improve and optimize your site 
  • Making smarter, data-driven decisions so your company can thrive, rather than risk being left behind

  Analytics is used to answer questions like :

  • Where are your website visitors coming from (location) ?
  • How many people visit your website ?
  • Which are the most popular pages on your site ?
  • What sources of traffic are coming to your site (social, marketing campaigns, search) ?
  • Is your marketing campaign performing better this month compared to last ?

  Matomo can answer all of the above questions. BONUS : On top of that, with Matomo you get the peace of mind knowing you’re the only one who has access to those answers.

  

  Matomo Analytics vs Google Analytics on WordPress

  The top 5 most useful features in Matomo Analytics that’s comparable to GA

  1. Campaign measurement – traffic. Matomo also has a URL builder that lets you track which campaigns are working effectively
  2. Tracking goals. Matomo empowers you to set goals you can track. Being able to see this means you can accurately measure your return on investment (ROI) 
  3. Audience reports to learn about visitors. Matomo’s powerful visitors feature lets you learn who is visiting your site, what their journey is and the steps they take to conversion.
  4. In depth view of behaviour with Funnels in Matomo. This tracks the journey of your visitors from the moment they enter your site, to when they leave. Giving you insight into where and why you lose your visitors.
  5. Custom reports. Where you create your unique reports to fit your business goals.

  Other benefits of using Matomo :

  • No data sampling which means you get 100% accurate reporting
  • 100% data ownership
  • Free Tag Manager
  • Search engine keyword rankings
  • Unlimited websites
  • Unlimited team members
  • GDPR manager
  • API access
  • Hosted on your own servers so you have full control over where your data is stored

  Learn more about the differences in this comprehensive table.

  

  Matomo Analytics for WordPress is free !

  Matomo Analytics is the best free Google Analytics alternative on the WordPress Directory. In addition to having comparable features where you can do pretty much do everything you wanted to do in GA. Matomo Analytics for WordPress makes for an ethical choice because you can respect your visitor’s privacy, can become GDPR compliant, and maintain control over your own data.

  Google Analytics leads the market for good reasons. It’s a great free tool for those who want analytics, but there’s no clarity when it comes to grey areas like privacy and data ownership. If these are major concerns for you, Matomo offers complete peace of mind that you’re doing the best you can to stay ethical while growing your business and website.

  It’s just as easy to install in a few click !

  Install and see for yourself !

• Marketing Cohort Analysis : How To Do It (With Examples)

  12 janvier 2024, par Erin

  The better you understand your customers, the more effective your marketing will become. 

  The good news is you don’t need to run expensive focus groups to learn much about how your customers behave. Instead, you can run a marketing cohort analysis using data from your website analytics.

  A marketing cohort groups your users by certain traits and allows you to drill down to discover why they take the actions on your website they do. 

  In this article, we’ll explain what a marketing cohort analysis is, show you what you can achieve with this analytical technique and provide a step-by-step guide to pulling it off. 

  What is cohort analysis in marketing ?

  A marketing cohort analysis is a form of behavioural analytics where you analyse the behavioural patterns of users who share a similar trait to better understand their actions. 

  These shared traits could be anything like the date they signed up for your product, users who bought your service through a paid ad or email subscribers from the United Kingdom.

  It’s a fantastic way to improve your marketing efforts, allowing you to better understand complex user behaviours, personalise campaigns accordingly and improve your ROI. 

  You can run marketing analysis using an analytics platform like Google Analytics or Matomo. With these platforms, you can measure how cohorts perform using traffic, engagement and conversion metrics.

  

  There are two types of cohort analysis : acquisition-based cohort analysis and behavioural-based cohort analysis.

  Acquisition-based cohort analysis

  An acquisition-based cohort divides users by the date they purchased your product or service and tracks their behaviour afterward. 

  For example, one cohort could be all the users who signed up for your product in November. Another could be the users who signed up for your product in October. 

  You could then run a cohort analysis to see how the behaviour of the two cohorts differed. 

  Did the November cohort show higher engagement rates, increased frequency of visits post-acquisition or quicker conversions compared to the October cohort ? Analysing these cohorts can help with refining marketing strategies, optimising user experiences and improving retention and conversion rates.

  As you can see from the example, acquisition-based cohorts are a great way to track the initial acquisition and how user behaviour evolves post-acquisition.

  Behavioural-based cohort analysis

  A behavioural-based cohort divides users by their actions on your site. That could be their bounce rate, the number of actions they took on your site, their average time on site and more.

  

  Behavioural cohort analysis gives you a much deeper understanding of user behaviour and how they interact with your website.

  What can you achieve with a marketing cohort analysis ?

  A marketing cohort analysis is a valuable tool that can help marketers and product teams achieve the following goals :

  

  Understand which customers churn and why

  Acquisition and behavioural cohort analyses help marketing teams understand when and why customers leave. This is one of the most common goals of a marketing cohort analysis. 

  Learn which customers are most valuable

  Want to find out which channels create the most valuable customers or what actions customers take that increase their loyalty ? You can use a cohort analysis to do just that. 

  For example, you may find out you retain users who signed up via direct traffic better than those that signed up from an ad campaign. 

  Discover how to improve your product

  You can even use cohort analysis to identify opportunities to improve your website and track the impact of your changes. For example, you could see how visitor behaviour changes after a website refresh or whether visitors who take a certain action make more purchases. 

  Find out how to improve your marketing campaign

  A marketing cohort analysis makes it easy to find out which campaigns generate the best and most profitable customers. For example, you can run a cohort analysis to determine which channel (PPC ads, organic search, social media, etc.) generates customers with the lowest churn rate. 

  If a certain ad campaign generates the low-churn customers, you can allocate a budget accordingly. Alternatively, if customers from another ad campaign churn quickly, you can look into why that may be the case and optimise your campaigns to improve them. 

  Measure the impact of changes

  You can use a behavioural cohort analysis to understand what impact changes to your website or product have on active users. 

  If you introduced a pricing page to your website, for instance, you could analyse the behaviour of visitors who interacted with that page compared to those who didn’t, using behavioural cohort analysis to gauge the impact of these website changes on engagemen or conversions.

  The problem with cohort analysis in Google Analytics

  Google Analytics is often the first platform marketers turn to when they want to run a cohort analysis. While it’s a free solution, it’s not the most accurate or easy to use and users often encounter various issues. 

  For starters, Google Analytics can’t process user visitor data if they reject cookies. This can lead to an inaccurate view of traffic and compromise the reliability of your insights.

  In addition, GA is also known for sampling data, meaning it provides a subset rather than the complete dataset. Without the complete view of your website’s performance, you might make the wrong decisions, leading to less effective campaigns, missed opportunities and difficulties in reaching marketing goals.

  How to analyse cohorts with Matomo

  Luckily, there is an alternative to Google Analytics. 

  As the leading open-source web analytics solution, Matomo offers a robust option for cohort analysis. With its 100% accurate data, thanks to the absence of sampling, and its privacy-friendly tracking, users can rely on the data without resorting to guesswork. It is a premium feature included with our Matomo Cloud or available to purchase on the Matomo Marketplace for Matomo On-Premise users.

  Below, we’ll show how you can run a marketing cohort analysis using Matomo.

  Set a goal

  Setting a goal is the first step in running a cohort analysis with any platform. Define what you want to achieve from your analysis and choose the metrics you want to measure. 

  For example, you may want to improve your customer retention rate over the first 90 days. 

  Define cohorts

  Next, create cohorts by defining segmentation criteria. As we’ve discussed above, this could be acquisition-based or behavioural. 

  Matomo makes it easy to define cohorts and create charts. 

  In the sidebar menu, click Visitors > Cohorts. You’ll immediately see Matomo’s standard cohort report (something like the one below).

  

  In the example above, we’ve created cohorts by bounce rate. 

  You can view cohorts by weekly, monthly or yearly periods using the date selector and change the metric using the dropdown. Other metrics you can analyse cohorts by include :

  • Unique visitors
  • Return visitors
  • Conversion rates
  • Revenue
  • Actions per visit

  Change the data selection to create your desired cohort, and Matomo will automatically generate the report. 

  Try Matomo for Free

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

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  Analyse your cohort chart

  Cohort charts can be intimidating initially, but they are pretty easy to understand and packed with insights. 

  Here’s an example of an acquisition-based cohort chart from Matomo looking at the percentage of returning visitors :

  

  Cohorts run vertically. The oldest cohort (visitors between February 13 – 19) is at the top of the chart, with the newest cohort (April 17 – 23) at the bottom. 

  The period of time runs horizontally — daily in this case. The cells show the corresponding value for the metric we’re plotting (the percentage of returning visitors). 

  For example, 98.69% of visitors who landed on your site between February 13 – 19, returned two weeks later. 

  Usually, running one cohort analysis isn’t enough to identify a problem or find a solution. That’s why comparing several cohort analyses or digging deeper using segmentation is important.

  Segment your cohort chart

  Matomo lets you dig deeper by segmenting each cohort to examine their behaviour’s specifics. You can do this from the cohort report by clicking the segmented visitor log icon in the relevant row.

  

  

  Segmenting cohorts lets you understand why users behave the way they do. For example, suppose you find that users you purchased on Black Friday don’t return to your site often. In that case, you may want to rethink your offers for next year to target an audience with potentially better customer lifetime value. 

  Start using Matomo for marketing cohort analysis

  A marketing cohort analysis can teach you a lot about your customers and the health of your business. But you need the right tools to succeed. 

  Matomo provides an effective and privacy-first way to run your analysis. You can create custom customer segments based on almost anything, from demographics and geography to referral sources and user behaviour. 

  Our custom cohort analysis reports and colour-coded visualisations make it easy to analyse cohorts and spot patterns. Best of all, the data is 100% accurate. Unlike other web analytics solution or cohort analysis tools, we don’t sample data. 

  Find out how you can use Matomo to run marketing cohort analysis by trialling us free for 21 days. No credit card required.

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