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• Fatal Python error : could not acquire lock for at interpreter shutdown, possibly due to daemon threads

  3 octobre 2019, par JavNoor

  Running the following :

  import imageio
  
  
  
  class vidrdf:
  
      def __init__(self, vidfile):
  
          self.vid = imageio.get_reader(vidfile,  'ffmpeg')
  
  
  
  vidfile = 'movie.mov'
  
  rdfobj = vidrdf(vidfile)

  I get :

  Fatal Python error: could not acquire lock for <_io.BufferedReader name=8> at interpreter shutdown, possibly due to daemon threads
  
  
  
  Thread 0x00007fd2dc2a8700 (most recent call first):
  
    File “/path/miniconda3/envs/flower/lib/python3.7/site-packages/imageio_ffmpeg/_parsing.py", line 61 in run
  
    File "miniconda3/envs/flower/lib/python3.7/threading.py", line 926 in _bootstrap_inner
  
    File “/path/miniconda3/envs/flower/lib/python3.7/threading.py", line 890 in _bootstrap
  
  
  
  Current thread 0x00007fd2f0bb9700 (most recent call first):
  
    File “/path/miniconda3/envs/flower/lib/python3.7/subprocess.py", line 1704 in _communicate
  
    File “/path/miniconda3/envs/flower/lib/python3.7/subprocess.py", line 939 in communicate
  
    File “/path/miniconda3/envs/flower/lib/python3.7/site-packages/imageio_ffmpeg/_io.py", line 193 in read_frames
  
    File “/path/miniconda3/envs/flower/lib/python3.7/site-packages/imageio/plugins/ffmpeg.py", line 342 in _close
  
    File “/path/miniconda3/envs/flower/lib/python3.7/site-packages/imageio/core/format.py", line 252 in close
  
    File “/path/miniconda3/envs/flower/lib/python3.7/site-packages/imageio/core/format.py", line 241 in __del__
  
  Aborted (core dumped)

  It doesn’t happen if I try :

  import imageio
  
  
  
  class vidrdf:
  
      def __init__(self, vidfile):
  
          self.vid = imageio.get_reader(vidfile,  'ffmpeg')
  
  
  
  vidfile = 'movie.mov'
  
  vidrdf(vidfile)

  or

  import imageio
  
  
  
  class vidrdf:
  
      def __init__(self, vidfile):
  
          vid = imageio.get_reader(vidfile,  'ffmpeg')
  
  
  
  vidfile = 'movie.mov'
  
  rdfobj = vidrdf(vidfile)

  So this is clearly an issue with copying the object. I’ve searched about daemon threads, but since I’m using imageio directly I can’t figure out why and how to resolve it. I would appreciate any recommendations.

• ffmpeg download mp3 in chunks slower than whole file

  6 août 2021, par loretoparisi

  I'm programmatically downloading in Python mp3 file's as wav chunks, seeking at position with ss and t with ffmpeg resulting in a sequence of ffmpeg commands

  


  ffmpeg -i https://file-examples-com.github.io/uploads/2017/11/file_example_MP3_5MG.mp3 -acodec pcm_s16le -ac 1 -ar 44100 -ss 0:08:55.780000 -t 0:01:00.000000 -sn -vn -y -f wav pipe:1
ffmpeg -i https://file-examples-com.github.io/uploads/2017/11/file_example_MP3_5MG.mp3 -acodec pcm_s16le -ac 1 -ar 44100 -ss -ss 0:09:55.780000 -t 0:01:00.000000 -sn -vn -y -f wav pipe:1
ffmpeg -i https://file-examples-com.github.io/uploads/2017/11/file_example_MP3_5MG.mp3 -acodec pcm_s16le -ac 1 -ar 44100 -ss -ss -ss 0:10:55.780000 -t 0:01:00.000000 -sn -vn -y -f wav pipe:1
...


  


  executed via concurrent.futures.ThreadPoolExecutor in this way :

  


  with concurrent.futures.ThreadPoolExecutor(max_workers=NTHREADS) as executor:
        for i,cmd in enumerate(process_list):
            futures.append(executor.submit(downloader.chunk_download,(cmd,i)))

        for future in concurrent.futures.as_completed(futures):
            try:
                completed.append(future.result())
            except Exception as e:print(e)
    completed.sort() 
    for k,c in enumerate(completed):
        if not k:
            waveform = c[1]
        else:
            waveform = np.append(waveform,c[1])


  


  where chunk_download is concat the chunks into the whole waveform

  


  def chunk_download(self,args):
        cmd=args[0]
        i=args[1]
        print('Downloading chunk n° %i' % i)

        print( ' '.join(cmd))
        process = subprocess.run(cmd,
                            stdout=subprocess.PIPE,
                            stderr=subprocess.PIPE,
                            bufsize=10**8)
        #buffer, stderr = process.communicate()
        buffer = process.stdout
        stderr = process.stderr
        print('chunk n° %i DOWNLOADED' % i)
        # convert to signal
        chunk_waveform = np.frombuffer(buffer=buffer, dtype=np.uint16, offset=8*44)
        chunk_waveform = chunk_waveform.astype(self.dtype)  
        print(len(chunk_waveform)/44100) 

        return i,chunk_waveform


  


  this works ok, but if I download the whole mp3, executing the command in cmd like :

  


  ffmpeg -i https://file-examples-com.github.io/uploads/2017/11/file_example_MP3_5MG.mp3 -acodec pcm_s16le -ac 1 -ar 44100 -sn -vn -y -f wav pipe:1


  


  It will take less time than the concurrent chunks download for the same file length, while I would expect the opposite. Could be this related to ffmpeg threading option (param --thread ?). If not, why the concurrent chunks download operation is slower ?

  


• Understanding User Flow : Eight Practical Examples for Better UX

  4 octobre 2024, par Daniel Crough — Analytics Tips, UX

  Imagine trying to cook a complex dish without a recipe. You might have all the ingredients, but without a clear sequence of steps, you’re likely to end up with a mess rather than a masterpiece. Similarly, designing a website without understanding user flow is a recipe for confusion and lost conversions.

  User flows—the paths visitors take through your site—are the recipes for digital success. They provide a clear sequence of steps that guide users towards their goals, whether that’s making a purchase, signing up for a newsletter, or finding information. By understanding and optimising these flows, you can create a website that’s not just functional, but delightfully intuitive.

  In this article, we’ll explore seven practical user flow examples and show you how to apply these insights using web analytics tools. Whether you’re new to user experience (UX) design or a seasoned professional, you’ll find valuable takeaways to enhance your digital strategy.

  Read More