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• How can I retain 2x pixel density when encoding Retina screen capture with ffmpeg ?

  26 février 2018, par hfossli

  Whenever I use ffmpeg to encode a HiDPI/Retina screen recording, the video plays at 2x the size, so it looks fuzzy, because the pixel density is not retained.

  How can I retain the original pixel density of HiDPI screen recordings with ffmpeg ?

  How to reproduce :

  1. Use QuickTime Player to create a Screen Recording on a Retina Mac.
  2. Play the video you recorded in QuickTime Player using the ⌘1 Actual Size view. Notice that it’s playing 2:1 on your Retina Display, so the video looks sharp. It’s playing in half the space of the actual recorded pixels.

  3. Use ffmpeg to encode the video using a command like this :

     ffmpeg -i haha.mov -c:v libx264 -crf 23 haha-lg.mov

  4. Play the new ffmpeg-compressed video in QuickTime Player using the ⌘1 Actual Size view. Notice that it’s playing 1:1, so the video looks fuzzy.

  To clarify, the video does not look blurry because it was compressed. Rather, it looks blurry because the video is being played twice as big as it should be, at a 1:1 pixel density, instead of the required 2:1 pixel density, presumably because some metadata is being discarded when encoding.

  For the record, VLC plays both videos too big (blurry). So being able to play HiDPI videos seems to be a feature of QuickTime Player.

  Here is the detailed information ffmpeg shows for the original screen recording :

  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'haha.mov':
  
    Metadata:
  
      major_brand     : qt  
  
      minor_version   : 0
  
      compatible_brands: qt  
  
      creation_time   : 2018-02-26T16:46:00.000000Z
  
      com.apple.quicktime.make: Apple
  
      com.apple.quicktime.model: iMac18,3
  
      com.apple.quicktime.software: Mac OS X 10.13.3 (17D102)
  
      com.apple.quicktime.creationdate: 2018-02-26T10:45:50-0600
  
    Duration: 00:00:04.35, start: 0.000000, bitrate: 10947 kb/s
  
      Stream #0:0(und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv, bt709), 1396x928 [SAR 1:1 DAR 349:232], 10701 kb/s, 60 fps, 60 tbr, 6k tbn, 12k tbc (default)
  
      Metadata:
  
        creation_time   : 2018-02-26T16:46:00.000000Z
  
        handler_name    : Core Media Data Handler
  
        encoder         : H.264

  And here is the information for the ffmpeg-compressed version :

  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'haha-lg.mov':
  
    Metadata:
  
      major_brand     : qt  
  
      minor_version   : 512
  
      compatible_brands: qt  
  
      encoder         : Lavf57.83.100
  
    Duration: 00:00:04.35, start: 0.000000, bitrate: 1923 kb/s
  
      Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1396x928 [SAR 1:1 DAR 349:232], 1783 kb/s, 60 fps, 60 tbr, 15360 tbn, 120 tbc (default)
  
      Metadata:
  
        handler_name    : DataHandler
  
        encoder         : Lavc57.107.100 libx264

• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Matomo (Piwik) shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Matomo (Piwik) marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Matomo (Piwik) only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Matomo (Piwik) shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Matomo (Piwik) is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Matomo (Piwik) does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution

  The post Make better marketing decisions with attribution modeling appeared first on Analytics Platform - Matomo.

• FFMPEG command from Python 3.5 does not actually create audio file

  20 décembre 2017, par Nathan Blaine

  I have a Django web application that accepts user uploaded videos/audio and saves them into a folder ’../WebAppDirectory/media/recordings’.

  I am then using a speech to text API to get a rough transcription of the audio. This is working fine for .wav and .mp4 files, but the web app also accepts videos (.MOV) that I would like to first convert to .wav, then pass off to the API.

  Using ffmpeg from my command line like this

  ffmpeg -i C:\Users\Nathan\Desktop\MeetingRecorderWebAPP\media\recordings\upload_sample.MOV -ab 160k -ac 2 -ar 44100 -vn upload_sample.wav

  Correctly creates the .wav file from the original .MOV.

  However, when I run this from python with

  subprocess.check_call(command, shell=True)

  ffmpeg responds with

  File ’upload_sample.wav’ already exists. Overwrite ? [y/N]

  While Python tells me

  FileNotFoundError : [Errno 2] No such file or directory : ’C :\Users\Nathan\Desktop\MeetingRecorderWebAPP\media\recordings\upload_sample.wav’

  It is also worth noting that I do not see a ’upload_sample.wav’ file in the media/recordings/ directory.

  This leads me to believe that maybe Python and ffmpeg are looking in different folders, but I am not sure where I am going wrong. When I print the command from the subprocess.check_call and copy/paste it into cmd, the file is created as expected.

  Hoping someone with some experience with ffmpeg/Python subprocess can help shed some light ! Here are the files I am working with :

  Folder Structure

  DjangoWebApp
  
  |---media
  
  |---|---imgs
  
  |---|---recordings
  
  |---|---|---upload_sample.MOV
  
  |---uploaded_audio_to_text.py

  uploaded_audio_to_text.py

  import speech_recognition as sr
  
  from os import path
  
  import os
  
  import subprocess
  
  
  
  
  
  def speech_to_text(file_name):
  
      AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), 'media','recordings', file_name)
  
      print("Looking at path: ",AUDIO_FILE)
  
      # get extension
  
      AUDIO_FILE_EXT = os.path.splitext(AUDIO_FILE)[1]
  
  
  
      if(AUDIO_FILE_EXT == '.MOV'):
  
          print("File is not .wav: ", AUDIO_FILE_EXT, "found. Converting...")
  
          # We will use subprocess and ffmpeg to convert this .MOV file to .wav, so we can send to API
  
          temp_wav = os.path.splitext(file_name)[0] + '.wav'
  
          print("New audio file will be: ", temp_wav)
  
          # build CMD ffmpeg command
  
          command = "ffmpeg -i "
  
          command += AUDIO_FILE
  
          command += " -ab 160k -ac 2 -ar 44100 -vn "
  
          command += temp_wav
  
  
  
          print("Attempting to run this command: \n",command)
  
          print(subprocess.check_call(command, shell=True))
  
          print("Past Subprocess.call")
  
          AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), 'media','recordings', temp_wav)
  
          print("AUDIO_FILE now set to: ", AUDIO_FILE)
  
  
  
      else:
  
          # continue with what we are doing
  
          pass
  
  
  
  
  
      r = sr.Recognizer()
  
      with sr.AudioFile(AUDIO_FILE) as source:
  
          audio = r.record(source)  # read the entire audio file
  
          text_transcription = "Sentinel"
  
          # recognize speech using Microsoft Bing Voice Recognition
  
          BING_KEY = "MY_KEY_:)"
  
          try:
  
              text_transcription = r.recognize_bing(audio, key=BING_KEY)
  
          except sr.UnknownValueError:
  
              print("Microsoft Bing Voice Recognition could not understand audio")
  
          except sr.RequestError as e:
  
              print("Could not request results from Microsoft Bing Voice Recognition service; {0}".format(e))
  
  
  
      return text_transcription
  
  
  
  
  
  #my tests
  
  my_relative_file_path = "upload_sample.MOV"
  
  print(speech_to_text(my_relative_file_path))

  Console output (traceback and my print()’s)

  Looking at path:  C:\Users\Nathan\Desktop\MeetingRecorderWebAPP\media\recordings\upload_sample.MOV 
  
  File is not .wav:  .MOV found. Converting... 
  
  New audio file will be:  upload_sample.wav Attempting to run this command:
  
   ffmpeg -i C:\Users\Nathan\Desktop\MeetingRecorderWebAPP\media\recordings\upload_sample.MOV -ab 160k -ac 2 -ar 44100 -vn upload_sample.wav 
  
  ffmpeg version git-2017-12-18-74f408c Copyright (c) 2000-2017 the FFmpeg developers   built with gcc 7.2.0 (GCC)   
  
  ----REMOVED SOME FFMPEG OUTPUT FOR BREVITY----
  
  File 'upload_sample.wav' already exists. Overwrite ? [y/N] y 
  
  Stream mapping:   Stream #0:1 -> #0:0 (aac (native) -> pcm_s16le (native)) Press [q] to stop, [?] for help Output #0, wav, to 'upload_sample.wav':   Metadata:
  
      major_brand     : qt  
  
      minor_version   : 0
  
      compatible_brands: qt  
  
      com.apple.quicktime.creationdate: 2017-12-19T16:06:10-0500
  
      com.apple.quicktime.make: Apple
  
      com.apple.quicktime.model: iPhone 6
  
      com.apple.quicktime.software: 10.3.3
  
      ISFT            : Lavf58.3.100
  
      Stream #0:0(und): Audio: pcm_s16le ([1][0][0][0] / 0x0001), 44100 Hz, stereo, s16, 1411 kb/s (default)
  
      Metadata:
  
        creation_time   : 2017-12-19T21:06:11.000000Z
  
        handler_name    : Core Media Data Handler
  
        encoder         : Lavc58.8.100 pcm_s16le size=    1036kB time=00:00:06.01 bitrate=1411.3kbits/s speed=N/A     video:0kB audio:1036kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.007352% 
  
  0 
  
  Traceback (most recent call last): Past Subprocess.call   
  
  File "C:\Users\Nathan\Desktop\MeetingRecorderWebAPP\uploaded_audio_to_text.py", line 53, in <module> 
  
  AUDIO_FILE now set to:  C:\Users\Nathan\Desktop\MeetingRecorderWebAPP\media\recordings\upload_sample.wav
  
      print(speech_to_text(my_relative_file_path))   
  
  File "C:\Users\Nathan\Desktop\MeetingRecorderWebAPP\uploaded_audio_to_text.py", line 36, in speech_to_text
  
      with sr.AudioFile(AUDIO_FILE) as source:   
  
  File "C:\Users\Nathan\AppData\Local\Programs\Python\Python36-32\lib\site-packages\speech_recognition\__init__.py", line 203, in __enter__
  
      self.audio_reader = wave.open(self.filename_or_fileobject, "rb")   
  
  File "C:\Users\Nathan\AppData\Local\Programs\Python\Python36-32\lib\wave.py", line 499, in open
  
      return Wave_read(f)   
  
  File "C:\Users\Nathan\AppData\Local\Programs\Python\Python36-32\lib\wave.py", line 159, in __init__
  
      f = builtins.open(f, 'rb') 
  
  FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Nathan\\Desktop\\MeetingRecorderWebAPP\\media\\recordings\\upload_sample.wav'
  
  
  
  Process finished with exit code 1
  
  </module>