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• avutil : remove deprecated FF_API_HDR_VIVID_THREE_SPLINE

  19 février, par James Almer

  avutil : remove deprecated FF_API_HDR_VIVID_THREE_SPLINE
  Deprecated since 2023-03-17.
  Signed-off-by : James Almer <jamrial@gmail.com>
  

  • [DH] libavcodec/dynamic_hdr_vivid.c
  • [DH] libavutil/hdr_dynamic_vivid_metadata.h
  • [DH] libavutil/version.h

• ffmpeg conversion failed error while splitting a 1.57GB .tif file

  12 janvier 2023, par Minai

  I am running ffmpeg to split a large .tif image of dimensions 25966 * 64114 into grids 0f 256 * 256 pixels but am getting an error.

  


  I ran the ffmpeg command on an 1883 * 1361 pixel .jpg image named coral2

  


  Here is the image

  


  Using the following command :

  


  ffmpeg -i coral2.jpg -qscale:v 1 -vf "crop=256:256:0:256" coral2-0-256.jpg

  


  Here is the crop

  


  When I run the same command on a 1.57GB .tif image :

  


  image.tif -qscale:v 1 -vf "crop=256:256:0:256" image%01d.tif

  


  I get the following error :

  


  


  


  C :\Users\gwmin\Downloads\coral\southbayapal>ffmpeg -i image.tif -qscale:v 1 -vf "crop=256:256:0:256" image%01d.tif

  


  ffmpeg version 2023-01-12-git-fc263f073e-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 12.2.0 (Rev7, Built by MSYS2 project)

  


  configuration : —enable-gpl —enable-version3 —enable-static —disable-w32threads —disable-autodetect —enable-fontconfig —enable-iconv —enable-gnutls —enable-libxml2 —enable-gmp —enable-bzlib —enable-lzma —enable-libsnappy —enable-zlib —enable-librist —enable-libsrt —enable-libssh —enable-libzmq —enable-avisynth —enable-libbluray —enable-libcaca —enable-sdl2 —enable-libaribb24 —enable-libdav1d —enable-libdavs2 —enable-libuavs3d —enable-libzvbi —enable-librav1e —enable-libsvtav1 —enable-libwebp —enable-libx264 —enable-libx265 —enable-libxavs2 —enable-libxvid —enable-libaom —enable-libjxl —enable-libopenjpeg —enable-libvpx —enable-mediafoundation —enable-libass —enable-frei0r —enable-libfreetype —enable-libfribidi —enable-liblensfun —enable-libvidstab —enable-libvmaf —enable-libzimg —enable-amf —enable-cuda-llvm —enable-cuvid —enable-ffnvcodec —enable-nvdec —enable-nvenc —enable-d3d11va —enable-dxva2 —enable-libvpl —enable-libshaderc —enable-vulkan —enable-libplacebo —enable-opencl —enable-libcdio —enable-libgme —enable-libmodplug —enable-libopenmpt —enable-libopencore-amrwb —enable-libmp3lame —enable-libshine —enable-libtheora —enable-libtwolame —enable-libvo-amrwbenc —enable-libilbc —enable-libgsm —enable-libopencore-amrnb —enable-libopus —enable-libspeex —enable-libvorbis —enable-ladspa —enable-libbs2b —enable-libflite —enable-libmysofa —enable-librubberband —enable-libsoxr —enable-chromaprint

  


  libavutil 57. 43.100 / 57. 43.100
libavcodec 59. 56.100 / 59. 56.100
libavformat 59. 35.100 / 59. 35.100
libavdevice 59. 8.101 / 59. 8.101
libavfilter 8. 53.100 / 8. 53.100
libswscale 6. 8.112 / 6. 8.112
libswresample 4. 9.100 / 4. 9.100
libpostproc 56. 7.100 / 56. 7.100

  


  [tiff @ 000002c4799b6b00] [IMGUTILS @ 000000e4045fed40] Picture size 25966x64115 is invalid

  


  [tiff_pipe @ 000002c4799a3700] Could not find codec parameters for stream 0 (Video : tiff, rgba) : unspecified size
Consider increasing the value for the 'analyzeduration' (0) and 'probesize' (5000000) options
Input #0, tiff_pipe, from 'SouthBayApal.tif' :
Duration : N/A, bitrate : N/A
Stream #0:0 : Video : tiff, rgba, 25 fps, 25 tbr, 25 tbn
Stream mapping :
Stream #0:0 -> #0:0 (tiff (native) -> tiff (native))
Press [q] to stop, [?] for help
[tiff @ 000002c4799a5d40] [IMGUTILS @ 000000e404dff650] Picture size 25966x64115 is invalid

  


  Error while decoding stream #0:0 : Invalid argument&#xA;[graph 0 input from stream 0:0 @ 000002c479e0cd00] Unable to parse option value "0x0" as image size&#xA;[graph 0 input from stream 0:0 @ 000002c479e0cd00] Error setting option video_size to value 0x0.&#xA;[graph 0 input from stream 0:0 @ 000002c479e0cd00] Error applying generic filter options.&#xA;Error reinitializing filters !&#xA;Error while filtering : Invalid argument&#xA;Finishing stream 0:0 without any data written to it.&#xA;[graph 0 input from stream 0:0 @ 000002c479e0c800] Unable to parse option value "0x0" as image size&#xA;[graph 0 input from stream 0:0 @ 000002c479e0c800] Error setting option video_size to value 0x0.&#xA;[graph 0 input from stream 0:0 @ 000002c479e0c800] Error applying generic filter options.&#xA;Error configuring filter graph&#xA;Conversion failed ! <

  &#xA;

  Please help. My code only works on splitting crops of small images but I cannot crop large .tif images. How can I split using ffmpeg ?

  &#xA;

• Introducing the Data Warehouse Connector feature

  30 janvier, par Matomo Core Team

  Matomo is built on a simple truth : your data belongs to you, and you should have complete control over it. That’s why we’re excited to launch our new Data Warehouse Connector feature for Matomo Cloud, giving you even more ways to work with your analytics data. 

  Until now, getting raw data from Matomo Cloud required APIs and custom scripts, or waiting for engineering help.  

  Our new Data Warehouse Connector feature removes those barriers. You can now access your raw, unaggregated data and schedule regular exports straight to your data warehouse. 

  The feature works with all major data warehouses including (but not limited to) : 

  • Google BigQuery 
  • Amazon Redshift 
  • Snowflake 
  • Azure Synapse Analytics 
  • Apache Hive 
  • Teradata 

  You can schedule exports, combine your Matomo data with other data sources in your data warehouse, and easily query data with SQL-like queries. 

  Direct raw data access for greater data portability 

  Waiting for engineering support can delay your work. Managing API connections and writing scripts can be time-consuming. This keeps you from focusing on what you do best—analysing data. 

  

  With the Data Warehouse Connector feature, you get direct access to your raw Matomo data without the technical setup. So, you can spend more time analysing data and finding insights that matter. 

  Bringing your data together 

  Answering business questions often requires data from multiple sources. A single customer interaction might span your CRM, web analytics, sales systems, and more. Piecing this data together manually is time-consuming—what starts as a seemingly simple question from stakeholders can turn into hours of work collecting and comparing data across different tools. 

  This feature lets you combine your Matomo data with data from other business systems in your data warehouse. Instead of switching between tools or manually comparing spreadsheets, you can analyse all your data in one place to better understand how customers interact with your business. 

  Easy, custom analysis with SQL-like queries 

  Standard, pre-built reports often don’t address the specific, detailed questions that analysts need to answer.  

  When you use the Data Warehouse Connector feature, you can use SQL-like queries in your data warehouse to do detailed, customised analysis. This flexibility allows you to explore your data in depth and uncover specific insights that aren’t possible with pre-built reports. 

  

  Here is an example of how you might use SQL-like query to compare the behaviours of paying vs. non-paying users : 

  				
                                          <xmp>SELECT  
  
  
  
  custom_dimension_value AS user_type, -- Assuming 'user_type' is stored in a custom dimension 
  
  
  
  COUNT(*) AS total_visits,  
  
  
  
  AVG(visit_total_time) AS avg_duration, 
  
  
  
  SUM(conversion.revenue) AS total_spent  
  
  
  
  FROM  
  
  
  
  `your_project.your_dataset.matomo_log_visit` AS visit 
  
  
  
  LEFT JOIN  
  
  
  
  `your_project.your_dataset.matomo_log_conversion` AS conversion  
  
  
  
  ON  
  
  
  
  visit.idvisit = conversion.idvisit  
  
  
  
  GROUP BY  
  
  
  
  custom_dimension_value; </xmp>
  
                                  
  			

  This query helps you compare metrics such as the number of visits, average session duration, and total amount spent between paying and non-paying users. It provides a full view of behavioural differences between these groups. 

  Advanced data manipulation and visualisation 

  When you need to create detailed reports or dive deep into data analysis, working within the constraints of a fixed user interface (UI) can limit your ability to draw insights. 

  Exporting your Matomo data to a data warehouse like BigQuery provides greater flexibility for in-depth manipulation and advanced visualisations, enabling you to uncover deeper insights and tailor your reports more effectively. 

  Getting started 

  To set up data warehouse exports in your Matomo : 

  1. Go to System Admin (cog icon in the top right corner) 
  2. Select ‘Export’ from the left-hand menu 
  3. Choose ‘Data Warehouse Connector’ 

  You’ll find detailed instructions in our data warehouse exports guide.  

  Please note, enabling this feature will cost an additional 10% of your current subscription. You can view the exact cost by following the steps above. 

  New to Matomo ? Start your 21-day free trial now (no credit card required), or request a demo. 

  Try Matomo for Free

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