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• avcodec_decode_video2 fails to decode after frame resolution change

  10 avril 2021, par Krzysztof Kansy

  I'm using ffmpeg in Android project via JNI to decode real-time H264 video stream. On the Java side I'm only sending the the byte arrays into native module. Native code is running a loop and checking data buffers for new data to decode. Each data chunk is processed with :
  

  



  int bytesLeft = data->GetSize();
int paserLength = 0;
int decodeDataLength = 0;
int gotPicture = 0;
const uint8_t* buffer = data->GetData();
while (bytesLeft > 0) {
    AVPacket packet;
    av_init_packet(&packet);
    paserLength = av_parser_parse2(_codecPaser, _codecCtx, &packet.data, &packet.size, buffer, bytesLeft, AV_NOPTS_VALUE, AV_NOPTS_VALUE, AV_NOPTS_VALUE);
    bytesLeft -= paserLength;
    buffer += paserLength;

    if (packet.size > 0) {
        decodeDataLength = avcodec_decode_video2(_codecCtx, _frame, &gotPicture, &packet);
    }
    else {
        break;
    }
    av_free_packet(&packet);
}

if (gotPicture) {
// pass the frame to rendering
}


  



  The system works pretty well until incoming video's resolution changes. I need to handle transition between 4:3 and 16:9 aspect ratios. While having AVCodecContext configured as follows :

  



  _codecCtx->flags2|=CODEC_FLAG2_FAST;
_codecCtx->thread_count = 2;
_codecCtx->thread_type = FF_THREAD_FRAME;

if(_codec->capabilities&CODEC_FLAG_LOW_DELAY){
    _codecCtx->flags|=CODEC_FLAG_LOW_DELAY;
}


  



  I wasn't able to continue decoding new frames after video resolution change. The got_picture_ptr flag that avcodec_decode_video2 enables when whole frame is available was never true after that.
  
This ticket made me wonder if the issue isn't connected with multithreading. Only useful thing I've noticed is that when I change thread_type to FF_THREAD_SLICE the decoder is not always blocked after resolution change, about half of my attempts were successfull. Switching to single-threaded processing is not possible, I need more computing power. Setting up the context to one thread does not solve the problem and makes the decoder not keeping up with processing incoming data.
  
Everything work well after app restart.
  

  



  I can only think of one workoround (it doesn't really solve the problem) : unloading and loading the whole library after stream resolution change (e.g as mentioned in here). I don't think it's good tho, it will propably introduce other bugs and take a lot of time (from user's viewpoint).

  



  Is it possible to fix this issue ?

  



  EDIT :
  
I've dumped the stream data that is passed to decoding pipeline. I've changed the resolution few times while stream was being captured. Playing it with ffplay showed that in moment when resolution changed and preview in application froze, ffplay managed to continue, but preview is glitchy for a second or so. You can see full ffplay log here. In this case video preview stopped when I changed resolution to 960x720 for the second time. (Reinit context to 960x720, pix_fmt: yuv420p in log).

  


• VP8 for Real-time Video Applications

  15 février 2011, par noreply@blogger.com (John Luther)

  With the growing interest in videoconferencing on the web platform, it’s a good time to explore the features of VP8 that make it an exceptionally good codec for real-time applications like videoconferencing.

  VP8 Design History & Features

  Real-time applications were a primary use case when VP8 was designed. The VP8 encoder has features specifically engineered to overcome the challenges inherent in compressing and transmitting real-time video data.
  

  • Processor-adaptive encoding. 16 encoder complexity levels automatically (or manually) adjust encoder features such as motion search strategy, quantizer optimizations, and loop filtering strength.
  • Encoder can be configured to use a target percentage of the host CPU.
    Ability to measure the time taken to encode each frame and adjust encoder complexity dynamically to keep the encoding time per frame constant
  • Robust error recovery (packet retransmission, forward error correction, recovery frame/new keyframe requests)
  • Temporal scalability (i.e., a single video bitstream that can degrade as needed depending on a participant’s available bandwidth)
  • Highly efficient decoding performance on low-power devices. Conventional video technology has grown to a state of complexity where dedicated hardware chips are needed to make it work well. With VP8, software-based solutions have proven to meet customer needs without requiring specialized hardware.

  For a more information about real-time video features in VP8, see the slide presentation by WebM Project engineer Paul Wilkins (PDF file).

  Commercially Available Products

  Millions of people around the world have been using VP7/8 for video chat for years. VP8 is deployed in some of today’s most popular consumer videoconferencing applications, including Skype (group video calling), Sightspeed, ooVoo and Logitech Vid. All of these vendors are active WebM project supporters. VP8’s predecessor, VP7, has been used in Skype video calling since 2005 and is supported in the new Skype app for iPhone. Other real-time VP8 implementations are coming soon, including ooVoo, and VP8 will play a leading role in Google’s plans for real-time applications on the web platform.

  Real-time applications will be extremely important as the web platform matures. The WebM community has made significant improvements in VP8 for real-time use cases since our launch and will continue to do so in the future.

  John Luther is Product Manager of the WebM Project.

  

• Running a py script in the Cloud

  12 janvier 2018, par Anay Bose

  I’m new to Google’s cloud & Virtual Machine(VM) instances, and I need some clarifications on a couple of points. I have a python script ; it imports a long range of functions. I need to run those functions in parallel. I’m using multiprocessing and Process, not threads. These functions are basically image and media processors, and they use many other tools like FFMPEG, imagemagick and Avisynth in addition to a wide range of python modules, including moviepy. Now, I would like to run some 50 functions in parallel assigning a CPU for each process. Images, media and avi files are stored in seperate folders. I’m on Windows7 Core-i7 machine. So, need cloud computing power.

  Now, my question can I run such a python script/app in the cloud that requires a very complicated file system and non-python tools i.e. ffmpeg, avisynth and avi files ?

  Can Google VMs emulate my local machine and empower me with more cores and memory to run such a program ? if not, then what are my options ? Is their any tutorials that I can follow ? I need your suggestions. I have given below an example script and some codes to help facilitate your understanding about my situation.

  from __future__ import unicode_literals
  
  import youtube_dl
  
  import os
  
  import time
  
  import sys
  
  reload(sys)  
  
  sys.setdefaultencoding('utf-8')
  
  from multiprocessing import Process
  
  from utils import *
  
  
  
  from clip31 import VIDEO31
  
  from clip32 import VIDEO32
  
  from clip189 import VIDEO189
  
  from clip16 import VIDEO16
  
  from clip39 import VIDEO39
  
  
  
  
  
  if __name__== '__main__':
  
  
  
      # 1. CALLING A FUNCTION
  
      folder = "bodyforce3\\16"
  
      serial = "16"
  
      images = get_filepaths("../16")
  
      videos = get_filepaths("12__media")
  
      pngs = get_filepaths("../pngs")
  
  
  
      Process(target=VIDEO192, args=(folder, serial, color1, color2, color3, images, videos)).start()
  
  
  
  
  
      # 2. CALLING A FUNCTION
  
      folder = "bodyforce3\\20"
  
      serial = "20"
  
      images = get_filepaths("../20")
  
      videos = get_filepaths("18__media")
  
  
  
      Process(target=VIDEO32, args=(folder, serial, color1, color2, color3, images, videos)).start()
  
  
  
  
  
      # 3. CALLING A FUNCTION
  
      folder = "bodyforce3\\14"
  
      serial = "14"
  
      images = get_filepaths("../14")
  
      videos = get_filepaths("16__media")
  
  
  
      Process(target=VIDEO91, args=(folder, serial, color1, color2, color3, images, videos)).start()

  I copy avi files in functions like this :

  src = "clip50_files"
  
  src_files = os.listdir(src)
  
  for file_name in src_files:
  
      full_file_name = os.path.join(src, file_name)
  
      if (os.path.isfile(full_file_name)):
  
          shutil.copy(full_file_name, folder)

  I call ffmpeg commands like this, and they are included within py functions.

  ###########################
  
  #### FFMPEG OPERATIONS ####
  
  ###########################
  
  
  
  print "Starting FFMPEG operations ..."
  
  
  
  if os.path.isfile(os.path.join(folder, "bounce-(3).avi")):
  
      os.remove(os.path.join(folder, "bounce-(3).avi"))
  
  
  
  
  
  infile = folder + "/bounce-(3).avs"
  
  outfile = folder + "/bounce-(3).avi"
  
  codec = "rawvideo"
  
  pix_fmt = "bgra"
  
  
  
  try:
  
      subprocess.call(["ffmpeg",
  
                       "-i" ,infile, 
  
                       "-c:v" ,codec, 
  
                       "-pix_fmt", pix_fmt, 
  
                       outfile],
  
                      stdout=open(os.devnull, 'w'), 
  
                      stderr=subprocess.STDOUT)
  
  except subprocess.CalledProcessError as e:   
  
      #except subprocess.CalledProcessError as e:
  
      sys.exit(e.output)
  
  except OSError as e:
  
      sys.exit(e.strerror)
  
  
  
  
  
  print "FFMPEG operations ended"