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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

• Measuring success for your SEO content

  20 mars 2020, par Jake Thornton — Uncategorized
  With over a billion searches every day in search engines, it’s hard to underestimate the importance of having your business present on page one (ideally in positions 1 – 3) ranking for the keywords that impact your sales and conversions.

  "In 2019, Google received nearly 2.3 trillion searches and on page one alone, the first five organic results accounted for 67.60% of all the clicks."

  So how is your business performing when it comes to ranking in the crucial top three spots of search for your most important keywords ?

  Accurately measuring the success of your content

  Once you’ve done your keyword research, created compelling content, optimised it to be search-friendly, and hit ‘publish’, you then need to accurately measure the success of your efforts.

  4 tips for measuring the success of your SEO content

  1. Create a custom segment for "Visitors from Search Engines only"

  By creating this custom segment, you’ll be able to analyse the behavioural patterns of the visitors who found your website through a search engine. 

  This way you can use many of Matomo’s powerful features (Visitors, Behaviour, Acquisition, Ecommerce, Goals etc.) focused entirely on search engine visitors only.

  

  Once you’ve created this segment, you can begin to see key metrics like which entry pages are responsible for referring visitors to your website. For example : Visit Behaviour – Entry Pages, this is a great way to analyse your most effective SEO pages.You may be surprised at what pages currently bring in the most traffic.

  As well as discovering which content resonates with your search audience, you will also be able to create more content focused on your targeted audience. Do this by learning which locations your search visitors are from, which device they use, what time of the day they visited your website and much more.

  >> Learn more about creating custom segments

  2. Website visits, time on site, pages per session, and bounce rate.

  “The top four ranking factors are website visits, time on site, pages per session, and bounce rate.”

  These four metrics set the benchmark for your SEO success.

  First, you need to get as many of the ‘right’ users to see your content. If you feel you’ve exhausted channels such as social media, email and possibly paid posts ; think about who your ideal audience is. Where are they likely to hang out online ? Are there community groups or forum sites that are interested in what you’re writing about ? 

  Whatever the case, putting yourself out there and getting more traffic to your website will help show search engines that people are interested in your website. As a result, they’ll likely rank you higher for that.

  When we say getting more of the ‘right’ users, we mean users who are generally interested in the topic/subject you’re writing about and interested in the work you do. 

  

  This is important for the next three metrics – increasing users time on your website, increasing the amount of pages your users explore on your website, and reducing the overall bounce rate for users who leave your website in a matter of seconds.

  To evaluate these metrics, go to Behaviour – Pages in your Matomo and see how these metrics vary on previous posts or pages you’ve created. Which pages are already showing you the best results ? Why do they get the results ? Can you focus on creating more content like this ?

  Understanding what content is resonating with your users through these metrics is easy and is the starting point for measuring the success of your SEO content strategy.

  >> Learn more about the Behaviour feature

  3. Row Evolution

  The Row Evolution feature embedded within the Search Engine Keywords Performance plugin lets you see how your ranking positions have changed over time for your important keywords. It also lets you see how the incoming traffic, related to your keywords, has changed over time.

  

  This is valuable when measuring the changes you’ve made to your landing pages to see if it has a positive or negative effect on your ranking efforts. 

  This also lets you see how search engine algorithm changes affect your search rankings over time, and to see if the effects of these algorithm updates are temporary or long lasting.

  Row evolution allows you to report on keyword performance with ease. If you only check your insights once a week or once a fortnight, you’ll see how ranking positions for your important keywords have changed daily (or even weekly, monthly or yearly however you prefer.)

  >> Learn more about Row Evolution

  4. What results are you getting from the lesser known search engines ?

  "In 2019 (to date), Google accounted for just over 75% of all global desktop search traffic, followed by Bing at 9.97%, Baidu at 9.34%, and Yahoo at 2.77%."

  For most of us, we want to be ranking in the top three spots in Google Search because that’s where the majority of search users are. However, don’t shy away from opportunities you could be missing with lesser known search engines.

  If you sell a product aimed at 55-65 year olds who use a PC computer, chances are they are using Bing. If you have customers in China the majority will be using Baidu, or in our case at Matomo, many of our loyal users use a privacy-friendly search engine like DuckDuckGo or Qwant.

  Some of your ideal customers might be finding you through these alternative search engines, so be sure to measure the impact that these referrals may have on your conversions.

  

  Strategically including important keywords that impact your business

  While search is an important acquisition channel for most businesses, it’s also one of the most competitive.

  We recommend analysing your keyword and content performance regularly and alter content that isn’t performing as well as you’d like. You need to continually learn from the content that is successful, and focus on creating more content like this. 

  The final thing to remember with search keyword performance is to be patient. If you have had little success in the past with attracting customers through search, it can take time to build this reputation with search engines.

• On-premise analytics demand grows as Google Analytics GDPR uncertainties continue

  7 janvier 2020, par Jake Thornton — Privacy

  The Google Analytics GDPR relationship is a complicated one. Website owners in states like Berlin in Germany are now required to ask users for consent to collect their data. This doesn’t make for the friendliest user-experience and often the website visitor will simply click “no.”

  The problem Google Analytics now presents website owners in the EU is with more visitors clicking “no”, the less accurate your data will become.

  Why do you need to ask your visitors for consent ?

  At this stage it’s simply because Google Analytics collects data for its own purposes. An example of this is using your visitor’s personal data for retargeting purposes across their advertising platforms like Google Ads and YouTube. 

  Google’s Privacy & Terms states : “when you visit a website that uses advertising services like AdSense, including analytics tools like Google Analytics, or embeds video content from YouTube, your web browser automatically sends certain information to Google. This includes the URL of the page you’re visiting and your IP address. We may also set cookies on your browser or read cookies that are already there. Apps that use Google advertising services also share information with Google, such as the name of the app and a unique identifier for advertising.”

  

  The rise of hosting web analytics on-premise

  Managing Google Analytics and GDPR can quickly become complicated, so there’s been an increase in website owners switching from cloud-hosted web analytics platforms, like Google Analytics, to more GDPR compliant alternatives, where you can host web analytics software on your own servers. This is called hosting web analytics on-premise.

  Hosting web analytics on your own servers means :

  No third-parties are involved

  The visitor data your website collects is stored on your own internal infrastructure. This means no third-parties are involved and there’s no risk of personal data being used in the way Google Analytics uses it e.g. sending personal data to its advertising platforms. 

  When you sign up with Google Analytics you sign away control of your user’s personal data. With on-premise website analytics, you own your data and are in full control.

  NOTE : Though Google Analytics uses personal data for its own purposes, not all cloud hosted web analytics platforms do this. As an example, Matomo Analytics Cloud hosted solution states that all personal data collected is not used for its own purposes and that Matomo has no rights in accessing or using this personal data. 

  

  You control where in the world your personal data is stored

  Google Analytics servers are based out of USA, Europe and Asia, so where your personal data will end up is uncertain and you don’t have the option to choose which location it goes to when using free Google Analytics.

  Different countries have different laws when it comes to accessing personal data. When you choose to host your web analytics on-premise, you can choose the location of your servers and where the personal data is stored.

  More flexibility

  With self-hosted web analytics platforms like Matomo On-Premise, you can extend the platform to do anything you want without the restrictions that cloud hosted platforms impose.

  You can :

  • Get full access to the source code of open-source solutions, like Matomo
  • Extend the platform however you want for your business
  • Get access to APIs
  • Have no data limitations or restrictions
  • Get RAW data access
  • Have control over security

  >> Read more about on-premise flexibility for web analytics here

  

  So what does the future look like for Google Analytics and GDPR ?

  It’s difficult to assess this right now. How exactly GDPR is enforced is still quite unclear. 

  What is clear however, is now website owners in Berlin using Google Analytics are lawfully required to ask their visitors for consent to collect personal data. It has been reported that Google Analytics has already received 200,000 complaints in Germany alone and it appears this trend is likely to continue across much of the EU.

  When using Google Analytics in the EU you must also ensure your privacy policy is updated so website visitors are aware that data is being collected through Google Analytics for its own purposes.

  Moving to a web analytics on-premise platform

  Matomo Analytics is the #1 open-source web analytics platform in the world and has been rated as an exceptional alternative to Google Analytics. Check the reviews on Capterra.

  Choosing Matomo On-Premise means you can control exactly where your data is stored, you have full flexibility to customise the platform to do what you want and it’s FREE.

  Matomo’s mission is to give control back to website owners and the team has designed the platform so that moving away from Google Analytics is seamless. Matomo offers most of your favourite Google Analytics features, a leaner interface to navigate, and the option to add free and paid premium features that Google Analytics can’t even offer you.

  And now you can import your historical Google Analytics data directly into your Matomo with the Google Analytics Importer plugin.

  DOWNLOAD MATOMO (FREE)

  And if you can’t host web analytics on your own servers ...

  Hosting web analytics on-premise is not an option for all businesses as you do need the internal infrastructure and technical knowledge to host your own platform.

  If you can’t self-host, then Matomo has a Cloud hosted solution you can easily install and operate like Google Analytics, which is hosted on Matomo’s servers in the EU. 

  The GDPR advantages of choosing Matomo Cloud over Google Analytics are :

  • Servers are secure and based in the EU (strict laws forbid outside access)
  • 100% data ownership – we never use data for our own purposes
  • You can export your data anytime and switch to Matomo On-Premise whenever you like
  • User-privacy protection
  • Advanced GDPR Manager and data anonymisation features which GA doesn’t offer

  

  Interested to learn more ?

  If you are wanting to learn more about why users are making the move from Google Analytics to Matomo, check out our Matomo Analytics vs Google Analytics comparison page.

  >> Matomo Analytics vs Google Analytics