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• avcodec/libvpxenc : add a way to explicitly set temporal layer id

  10 février 2020, par Wonkap Jang

  avcodec/libvpxenc : add a way to explicitly set temporal layer id
  In order for rate control to correctly allocate bitrate to each temporal
  
  layer, correct temporal layer id has to be set to each frame. This
  
  commit provides the ability to set correct temporal layer id for each
  
  frame.
  Signed-off-by : James Zern <jzern@google.com>
  

  • [DH] doc/encoders.texi
  • [DH] libavcodec/libvpxenc.c
  • [DH] libavcodec/version.h

• Efficient real-time video stream processing and forwarding with RTMP servers

  19 mai 2023, par dumbQuestions

  I have a scenario where I need to retrieve a video stream from an RTMP server, apply image processing (specifically, adding blur to frames), and then forward the processed stream to another RTMP server (in this case, Twitch).

  


  Currently, I'm using ffmpeg in conjunction with cv2 to retrieve and process the stream. However, this approach introduces significant lag when applying the blur. I'm seeking an alternative method that can achieve the desired result more efficiently. I did attempt to solely rely on ffmpeg for the entire process, but I couldn't find a way to selectively process frames based on a given condition and subsequently transmit only those processed frames.

  


  Is there a more efficient approach or alternative solution that can address this issue and enable real-time video stream processing with minimal lag ?

  


  Thanks in advance !

  


  def forward_stream(server_url, stream_key, twitch_stream_key):
    get_ffmpeg_command = [...]

    send_ffmpeg_command [...]

    # Start get FFmpeg process
    read_process = subprocess.Popen(get_ffmpeg_command, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)

    # Start send FFmpeg process
    send_process = send_process = subprocess.Popen(send_ffmpeg_command, stdin=subprocess.PIPE, stderr=subprocess.DEVNULL)

    # Open video capture
    cap = cv2.VideoCapture(f'{server_url}')

    while True:
        # Read the frame
        ret, frame = cap.read()
        if ret:
            # Apply machine learning algorithm
            should_blur = machine_learning_algorithm(frame)

            # Apply blur if necessary
            if machine_learning_algorithm(frame):
                frame = cv2.blur(frame, (25, 25))

            # Write the frame to FFmpeg process
            send_process.stdin.write(frame.tobytes())
        else:
            break

    # Release resources
    cap.release()
    read_process.stdin.close()
    read_process.wait()



  


• (FFmpeg) VP9 Vaapi encoding to a .mp4 or .webm container from given official ffmpeg example

  13 mai 2021, par User800222

  I'm trying to implement vp9 hardware acceleration encoding process. I followed ffmpeg offical github's example (Here -> vaapi_encode.c).

  



  But given example only save a .yuv file to .h264 file, I would like to save the frames to either .mp4 or .webm container. And having the ability to control the quality, and etc.

  



  I'm not reading frames from a file, I'm collecting frames from a live feed. When having full 5 secs of frames from the live feed, encode those frames using vp9_vaapi to a 5 secs .mp4 file.

  



  I'm able to save all the 5 secs frames from my live feed to a .mp4 or .webm file, but they couldn't be played correctly (more precisely : keep loading, and I open).

  



  The result from the official site's example :

  



  

  



  The cpu encoded vp9 .mp4 file result :

  



  

  



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