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• Matomo’s privacy-friendly web analytics software named best of the year 2022

  25 janvier 2023, par Erin

  W3Tech names Matomo ‘Traffic Analysis Tool of the Year 2022’ in its Web Technologies of the Year list of technologies that gained the most sites

  Matomo, a world-leading open-source web analytics platform, is proud to announce that it has received W3Tech’s award for the best web analytics software in its Web Technologies of the Year 2022. Matomo is the first independent, open-source tool named Traffic Analysis Tool of the Year – with previous winners including Google Analytics and Facebook Pixel.


  W3Tech, a trusted source for web technology research, determines winners for its annual Web Technologies of the Year list by technologies that gained the most websites. W3Tech surveys usage across millions of websites globally – comparing the number of sites using a technology on January 1st of one year with the number of sites using it the following year.

  W3Tech commenting on the Traffic Analysis Tool winners, said : “Matomo, the privacy-focused open source analytics platform, is the traffic analysis tool of the year for the first time, while Google Analytics and the other previous winners all lost a bit of market share in 2022. The Chinese Baidu Analytics ranks second this year. Snowplow, another open source tool, is an unexpected third.”


  Matomo launched in 2007 as an open-source analytics alternative to Google Analytics, keeps businesses GDPR and CCPA-compliant. Matomo is trusted by over 1.4 million websites in 220 countries and is translated into over 50 languages.


  Matomo founder Matthieu Aubry says, “As the first independent, open-source traffic analysis tool to receive this recognition, Matomo is humbled and honoured to lead the charge for change. It’s a testament to the hard work of our community, and it’s a clear sign that consumers and organisations are looking for ethical alternatives.


  “This recognition is a major win for the entire privacy movement and proves that the tide is turning against the big tech players who I believe have long prioritised profits over privacy. We are committed to continuing our work towards a more private and secure digital landscape for all.”


  In W3Tech’s Web Technologies of the Year 2022, Matomo was also judged third Tag Manager, behind Google Tag Manager and Adobe DTM.


  Matomo helps businesses and organisations track and optimise their online presence allowing users to easily collect, analyse, and act on their website and marketing data to gain a deeper understanding of their visitors and drive conversions and revenue. With 100% data ownership, customers using the company’s tools get the power to protect their website user’s privacy – and where their data is stored and what’s happening to it, without external influence. Furthermore, as the data is not sampled, it maintains data accuracy. 


  Aubry says its recent award is a positive reminder of how well this solution is performing internationally and is a testament to the exceptional quality and performance of Matomo’s powerful web analytics tools that respect a user’s privacy.


  “In 2020, the CJEU ruled US cloud servers don’t comply with GDPR. Then in 2022, the Austrian Data Protection Authority and French Data Protection Authority (CNIL) ruled that the use of Google Analytics is illegal due to data transfers to the US. With Matomo Cloud, the customer’s data is stored in Europe, and no data is transferred to the US. On the other hand, with Matomo On-Premise, the data is stored in your country of choice.


  “Matomo has also become one of the most popular open-source alternatives to Google Analytics for website owners and marketing teams because it empowers web professionals to make business decisions. Website investment, collateral, and arrangement are enriched by having the full picture and control of the data.”

  

  About Matomo

  Matomo is a world-leading open-source web analytics platform, trusted by over 1.4 million websites in 220 countries and translated into over 50 languages. Matomo helps businesses and organisations track and optimise their online presence allowing users to easily collect, analyse, and act on their website and marketing data to gain a deeper understanding of their visitors and drive conversions and revenue. Matomo’s vision is to create, as a community, the leading open digital analytics platform that gives every user complete control of their data.

  For more information/ press enquiries Press – Matomo

• OpenCV compilation error in Fedora 21

  6 mars 2015, par eap

  I’ve got OpenCV source code from github and I get the following error when trying to compile it :

  /lib64/libavutil.so.54: undefined reference to `clReleaseMemObject@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clReleaseCommandQueue@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clCreateBuffer@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clBuildProgram@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clSetKernelArg@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clGetDeviceIDs@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clEnqueueUnmapMemObject@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clGetPlatformInfo@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clCreateProgramWithSource@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clGetDeviceInfo@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clReleaseContext@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clCreateContextFromType@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clCreateCommandQueue@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clEnqueueMapBuffer@OPENCL_1.0'
  
  /lib64/libavutil.so.54: undefined reference to `clGetPlatformIDs@OPENCL_1.0'

  My machine is a laptop with Intel i7 and a GT630M graphics card and I’m using Bumblebee.

  Thanks.

• How to parallelize this for loop for rapidly converting YUV422 to RGB888 ?

  16 avril 2015, par vineet

  I am using v4l2 api to grab images from a Microsoft Lifecam and then transferring these images over TCP to a remote computer. I am also encoding the video frames into a MPEG2VIDEO using ffmpeg API. These recorded videos play too fast which is probably because not enough frames have been captured and due to incorrect FPS settings.

  The following is the code which converts a YUV422 source to a RGB888 image. This code fragment is the bottleneck in my code as it takes nearly 100 - 150 ms to execute which means I can’t log more than 6 - 10 FPS at 1280 x 720 resolution. The CPU usage is 100% as well.

  for (int line = 0; line < image_height; line++) {
  
      for (int column = 0; column < image_width; column++) {
  
          *dst++ = CLAMP((double)*py + 1.402*((double)*pv - 128.0));                                                  // R - first byte           
  
          *dst++ = CLAMP((double)*py - 0.344*((double)*pu - 128.0) - 0.714*((double)*pv - 128.0));    // G - next byte
  
          *dst++ = CLAMP((double)*py + 1.772*((double)*pu - 128.0));                                                            // B - next byte
  
  
  
          vid_frame->data[0][line * frame->linesize[0] + column] = *py; 
  
  
  
          // increment py, pu, pv here
  
  
  
      }

  ’dst’ is then compressed as jpeg and sent over TCP and ’vid_frame’ is saved to the disk.

  How can I make this code fragment faster so that I can get atleast 30 FPS at 1280x720 resolution as compared to the present 5-6 FPS ?

  I’ve tried parallelizing the for loop across three threads using p_thread, processing one third of the rows in each thread.

  for (int line = 0; line < image_height/3; line++) // thread 1
  
  for (int line = image_height/3; line < 2*image_height/3; line++) // thread 2
  
  for (int line = 2*image_height/3; line < image_height; line++) // thread 3

  This gave me only a minor improvement of 20-30 milliseconds per frame.
  What would be the best way to parallelize such loops ? Can I use GPU computing or something like OpenMP ? Say spwaning some 100 threads to do the calculations ?

  I also noticed higher frame rates with my laptop webcam as compared to the Microsoft USB Lifecam.

  Here are other details :

  • Ubuntu 12.04, ffmpeg 2.6
  • AMG-A8 quad core processor with 6GB RAM
  • Encoder settings :
    • codec : AV_CODEC_ID_MPEG2VIDEO
    • bitrate : 4000000
    • time_base : (AVRational)1, 20
    • pix_fmt : AV_PIX_FMT_YUV420P
    • gop : 10
    • max_b_frames : 1