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• FFmpeg error : ratecontrol_init : can't open stats file

  6 octobre 2017, par oldo.nicho

  I’ve setup an AWS EC2 instance running Ubuntu 14.04 and have installed FFmpeg so that I can compress and transcode video.

  I’m trying to do a two pass conversion with the following code :

  ffmpeg -i input-file.avi -codec:v libx264 -profile:v high -preset slow -b:v 500k -maxrate 500k -bufsize 1000k -vf scale=702:-1 -threads 0 -pass 1 -an -f mp4 ~/encoded/null

  and second pass :

  ffmpeg -i input-file.avi -codec:v libx264 -profile:v high -preset slow -b:v 500k -maxrate 500k -bufsize 1000k -vf scale=702:-1 -threads 0 -pass 2 -codec:a libfdk_aac -b:a 128k -f mp4 output-file.mp4

  However I get the following error :

  ffmpeg version N-77283-g91c2a33 Copyright (c) 2000-2015 the FFmpeg developers
  
    built with gcc 4.8 (Ubuntu 4.8.4-2ubuntu1~14.04)
  
    configuration: --prefix=/home/ubuntu/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/ubuntu/ffmpeg_build/include --extra-ldflags=-L/home/ubuntu/ffmpeg_build/lib --bindir=/home/ubuntu/bin --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libtheora --enable-libvorbis --enable-libx264 --enable-nonfree
  
    libavutil      55. 11.100 / 55. 11.100
  
    libavcodec     57. 17.100 / 57. 17.100
  
    libavformat    57. 20.100 / 57. 20.100
  
    libavdevice    57.  0.100 / 57.  0.100
  
    libavfilter     6. 21.100 /  6. 21.100
  
    libswscale      4.  0.100 /  4.  0.100
  
    libswresample   2.  0.101 /  2.  0.101
  
    libpostproc    54.  0.100 / 54.  0.100
  
  Input #0, avi, from 'input-file.avi':
  
    Duration: 01:18:05.29, start: 0.000000, bitrate: 2025 kb/s
  
      Stream #0:0: Video: mpeg4 (Simple Profile) (XVID / 0x44495658), yuv420p, 720x480 [SAR 1:1 DAR 3:2], 1789 kb/s, 29.97 fps, 29.97 tbr, 29.97 tbn, 29.97 tbc
  
      Stream #0:1: Audio: ac3 ([0] [0][0] / 0x2000), 48000 Hz, stereo, fltp, 224 kb/s
  
  [libx264 @ 0x1e04240] using SAR=1/1
  
  [libx264 @ 0x1e04240] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX AVX2 FMA3 LZCNT BMI2
  
  [libx264 @ 0x1e04240] ratecontrol_init: can't open stats file
  
  Output #0, mp4, to '/home/ubuntu/encoded/null':
  
      Stream #0:0: Video: h264, none, q=2-31, 128 kb/s, SAR 1:1 DAR 0:0, 29.97 fps
  
      Metadata:
  
        encoder         : Lavc57.17.100 libx264
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (mpeg4 (native) -> h264 (libx264))
  
  Error while opening encoder for output stream #0:0 - maybe incorrect parameters such as bit_rate, rate, width or height

  The command as written above works fine on my local computer (running OSX). Would anyone have any suggestions as to how to fix this problem ?

• ffmpeg - Concat multi mp4 files with audio file not working

  29 mars 2017, par Thanh Dao

  I follow this thread to concat multi mp4 files with audio file.
  But its not success. Have a lots error notifications had been displayed. I dont know how to fix it.
  Below is my command :

  "ffmpeg" -f concat -safe 0 \
  
  -i /path/to/text.txt \
  
  -i /path/to/audio.mp3 -vsync vfr -vf scale="640:640" -pix_fmt yuv420p \
  
  /path/to/output.mp4 2>&1

  The detail contents of text.txt

  file '/path/to/file1.mp4'
  
  file '/path/to/file2.mp4'
  
  file '/path/to/file3.mp4'
  
  file '/path/to/file4.mp4'
  
  file '/path/to/file5.mp4'
  
  file '/path/to/file6.mp4'
  
  file '/path/to/file7.mp4'
  
  file '/path/to/file8.mp4'
  
  file '/path/to/file9.mp4'
  
  file '/path/to/file10.mp4'
  
  file '/path/to/file11.mp4'
  
  file '/path/to/file12.mp4'
  
  file '/path/to/file13.mp4'
  
  file '/path/to/file14.mp4'
  
  file '/path/to/file15.mp4'
  
  file '/path/to/file16.mp4'
  
  file '/path/to/file17.mp4'
  
  file '/path/to/file18.mp4'

  And some lines of output errors :

  [concat @ 0x357e620] DTS 192000 < 229888 out of order
  
  [h264 @ 0x36920e0] top block unavailable for requested intra mode -1
  
  [h264 @ 0x36920e0] error while decoding MB 32 0
  
  [h264 @ 0x36920e0] concealing 2025 DC, 2025 AC, 2025 MV errors in I frame
  
  [h264 @ 0x36b7a80] concealing 1449 DC, 1449 AC, 1449 MV errors in P frame
  
  [h264 @ 0x36ff440] corrupted macroblock 26 1 (total_coeff=-1)
  
  [h264 @ 0x36ff440] error while decoding MB 26 1
  
  [h264 @ 0x36ff440] concealing 2003 DC, 2003 AC, 2003 MV errors in P frame
  
  [h264 @ 0x371af40] concealing 1456 DC, 1456 AC, 1456 MV errors in P frame
  
  [h264 @ 0x3736a40] ref 5 overflow
  
  [h264 @ 0x3736a40] error while decoding MB 1 1
  
  [h264 @ 0x3736a40] concealing 2025 DC, 2025 AC, 2025 MV errors in P frame
  
  [h264 @ 0x3752520] concealing 1449 DC, 1449 AC, 1449 MV errors in P frame
  
  [h264 @ 0x376dfa0] P sub_mb_type 8 out of range at 2 1
  
  [h264 @ 0x376dfa0] error while decoding MB 2 1
  
  [h264 @ 0x376dfa0] concealing 2025 DC, 2025 AC, 2025 MV errors in P frame
  
  [h264 @ 0x37a55a0] ref 6 overflow
  
  [h264 @ 0x37a55a0] error while decoding MB 3 1
  
  [h264 @ 0x37a55a0] concealing 2025 DC, 2025 AC, 2025 MV errors in P frame
  
  [h264 @ 0x3789aa0] concealing 1449 DC, 1449 AC, 1449 MV errors in P frame
  
  [h264 @ 0x36b7a80] ref 5 overflow
  
  [h264 @ 0x36b7a80] error while decoding MB 4 1
  
  [h264 @ 0x36b7a80] concealing 2025 DC, 2025 AC, 2025 MV errors in P frame
  
  [h264 @ 0x36920e0] concealing 1449 DC, 1449 AC, 1449 MV errors in P frame

• 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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