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• How to parallelize this for loop for rapidly converting YUV422 to RGB888 ?

  16 avril 2015, par vineet

  I am using v4l2 api to grab images from a Microsoft Lifecam and then transferring these images over TCP to a remote computer. I am also encoding the video frames into a MPEG2VIDEO using ffmpeg API. These recorded videos play too fast which is probably because not enough frames have been captured and due to incorrect FPS settings.

  The following is the code which converts a YUV422 source to a RGB888 image. This code fragment is the bottleneck in my code as it takes nearly 100 - 150 ms to execute which means I can’t log more than 6 - 10 FPS at 1280 x 720 resolution. The CPU usage is 100% as well.

  for (int line = 0; line < image_height; line++) {
  
      for (int column = 0; column < image_width; column++) {
  
          *dst++ = CLAMP((double)*py + 1.402*((double)*pv - 128.0));                                                  // R - first byte           
  
          *dst++ = CLAMP((double)*py - 0.344*((double)*pu - 128.0) - 0.714*((double)*pv - 128.0));    // G - next byte
  
          *dst++ = CLAMP((double)*py + 1.772*((double)*pu - 128.0));                                                            // B - next byte
  
  
  
          vid_frame->data[0][line * frame->linesize[0] + column] = *py; 
  
  
  
          // increment py, pu, pv here
  
  
  
      }

  ’dst’ is then compressed as jpeg and sent over TCP and ’vid_frame’ is saved to the disk.

  How can I make this code fragment faster so that I can get atleast 30 FPS at 1280x720 resolution as compared to the present 5-6 FPS ?

  I’ve tried parallelizing the for loop across three threads using p_thread, processing one third of the rows in each thread.

  for (int line = 0; line < image_height/3; line++) // thread 1
  
  for (int line = image_height/3; line < 2*image_height/3; line++) // thread 2
  
  for (int line = 2*image_height/3; line < image_height; line++) // thread 3

  This gave me only a minor improvement of 20-30 milliseconds per frame.
  What would be the best way to parallelize such loops ? Can I use GPU computing or something like OpenMP ? Say spwaning some 100 threads to do the calculations ?

  I also noticed higher frame rates with my laptop webcam as compared to the Microsoft USB Lifecam.

  Here are other details :

  • Ubuntu 12.04, ffmpeg 2.6
  • AMG-A8 quad core processor with 6GB RAM
  • Encoder settings :
    • codec : AV_CODEC_ID_MPEG2VIDEO
    • bitrate : 4000000
    • time_base : (AVRational)1, 20
    • pix_fmt : AV_PIX_FMT_YUV420P
    • gop : 10
    • max_b_frames : 1

• Evolution #3483 : #FORMULAIRE_MOT_DE_PASSE dans la dist

  27 juin 2018, par Anonyme

  Tiens, j’avais oublié cette fiche faite par Maieul à ma demande lorsque j’avais pas de compte. Toutes mes fiches ayant déjà été fermées d’office.
  (style : on ferme A car on décide que c’est un doublon de B et vice versa)
  Celle-ci peut également être fermée, car comme je l’ai précisé dans cette conversation (https://contrib.spip.net/Pour-un-fonctionnement-communautaire-efficace-dans-la#forum492387) la boite a abandonné SPIP
  Quand à mes 4 sites familiaux, ils n’ont pas les milliers de personnes à gérer des précédents : je peux faire les modifications à la main :-)
  Me reste plus qu’à désactiver mon compte
  Encore merci Maieul
  Et bon vent à l’équipe

• How to Install FFmpeg on Databricks [closed]

  20 mai, par Riley Cohen

  I’m posting this to save others from the headache I went through getting ffmpeg running on Databricks. The process isn’t super intuitive, especially with Unity Catalog and volume-based restrictions, so here’s a clear step-by-step guide.

  


  Step #1 : In Notebook Add init script for ffmpeg

  


  dbutils.fs.put(
  "dbfs:/Volumes/xxxxxxx/default/init/install_ffmpeg.sh",
  """#!/bin/bash
apt-get update -y
apt-get install -y ffmpeg
""",
  overwrite=True
)


  


  Step #2 Add init script to allowed list

  


  Follow this article :
https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/manage-privileges/privileges#manage-allowlist

  


  

  


  Step #3 Add the init script in the cluster advanced setting

  


  After creating this script, go to your cluster settings in Databricks UI (Clusters > Edit > Advanced Options > Init Scripts) and add the script path (dbfs :/Volumes/xxxxxxx/default/init/install_ffmpeg.sh). Restart the cluster to apply it. Once the cluster starts with this init script, FFmpeg will be installed and available on each node

  


  Step 4 : Start/Restart the cluster