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• Re-Stream a MPEG2 TS PAL Stream with crtmpserver

  2 octobre 2011, par mblaettermann

  I want to build up some kind of stream wrapper :

  I own an old Dreambox PAL Sat Reciever with Networking. This stream I want to transcode to a lower resultion an restream it.

  My Goal is, to have a simple website, where this stream is embedded via rtmp.

  I thougt crtmpserver should be the right software. For now I have a site running and can play local files through jwplayer/crtmpserver.

  I am looking for a solution for this :

  httpUrl -> ffmpeg -> crtmpserver

  Is that possible ? May I redirect the output of ffmpeg to a filed pipe, and crtmpserver could grab that ? Or go with UDP ?

  Any hints appreciated !!! Thanks !!

• iPhone HTTP Streaming .m3u8 and .ts files - how to create using ffmpeg

  8 septembre 2013, par ChessDev

  I'm trying to get apple-validated http media streams using ffmpeg and am getting errors. Here are some error examples :

  WARNING : Playlist Content-Type is 'application/x-mpegurl', but should
  be one of 'application/vnd.apple.mpegurl', 'audio/x-mpegurl' or
  'audio/mpegurl'.

  WARNING : 258 samples (88.966 %) do not have timestamps in track 256
  (avc1). 4 : us2-1.ts
   

  WARNING : Media segment duration outside of expected duration by 47.733
   % (5.23 vs. 10.00 seconds, limit is 20 %). 40 : us2-19.ts
   

  Average segment duration: 10.16 seconds
  
  Average segment bitrate: 320.12 kbit/s
  
  Average segment structural overhead: 175.89 kbit/s (54.94 %)
  
  
  
  Video codec: avc1
  
  Video resolution: 320x320 pixels
  
  Video frame rate: 29.72, 29.78, 29.82, 30.00, 29.64 fps
  
  Average video bitrate: 100.66 kbit/s
  
  H.264 profile: Baseline
  
  H.264 level: 3.0
  
  
  
  Audio codec: aac 
  
  Audio sample rate: 48000 Hz
  
  Average audio bitrate: 43.57 kbit/s

  ---

  Here is the end file I've been submitting : http://files.chesscomfiles.com/images_users/using/us2.m3u8

  Here is the file I used to create this : http://files.chesscomfiles.com/images_users/using/using-computers-1.mp4

  I've tried these commands, among others :

  ffmpeg -i using-computers-1.mp4 -f mpegts -acodec libfaac -ar 48000
  -ab 64k -s 320x320 -vcodec libx264 -vbsf h264_mp4toannexb -b 96k -flags +loop -cmp +chroma -partitions +parti4x4+partp8x8+partb8x8 -subq 5 -trellis 1 -refs 1 -coder 0 -me_range 16 -keyint_min 2 -sc_threshold 40 -i_qfactor 0.71 -bt 200k -maxrate 96k -bufsize 96k -rc_eq 'blurCplx^(1-qComp)' -qcomp 0.6 -qmin 10 -qmax 51 -qdiff 4 -level 30 -g 30 -async 2 us2.ts

  ffmpeg -i using-computers-1.mp4 -f mpegts -acodec libfaac -ar 48000
  -ab 64k -s 320x320 -vcodec libx264 -vbsf h264_mp4toannexb -b 96k -flags +loop -cmp +chroma -partitions +parti4x4+partp8x8+partb8x8 -subq 5 -trellis 1 -refs 1 -coder 0 -me_range 16 -keyint_min 25 -sc_threshold 40 -i_qfactor 0.71 -bt 200k -maxrate 96k -bufsize 96k -rc_eq 'blurCplx^(1-qComp)' -qcomp 0.6 -qmin 10 -qmax 51 -qdiff 4 -level 30 -g 30 -async 2 us1.ts

  ffmpeg -i using-computers-1.mp4 -vbsf h264_mp4toannexb -acodec copy -vcodec copy -f mpegts output.ts

  If someone can help me figure out what ffmpeg commands I should be running I'd really appreciate it !

• A Better Process Runner

  1er janvier 2011, par Multimedia Mike — Python

  I was recently processing a huge corpus of data. It went like this : For each file in a large set, run 'cmdline-tool <file>', capture the output and log results to a database, including whether the tool crashed. I wrote it in Python. I have done this exact type of the thing enough times in Python that I’m starting to notice a pattern.

  Every time I start writing such a program, I always begin with using Python’s commands module because it’s the easiest thing to do. Then I always have to abandon the module when I remember the hard way that whatever ’cmdline-tool’ is, it might run errant and try to execute forever. That’s when I import (rather, copy over) my process runner from FATE, the one that is able to kill a process after it has been running too long. I have used this module enough times that I wonder if I should spin it off into a new Python module.

  Or maybe I’m going about this the wrong way. Perhaps when the data set reaches a certain size, I’m really supposed to throw it on some kind of distributed cluster rather than task it to a Python script (a multithreaded one, to be sure, but one that runs on a single machine). Running the job on a distributed architecture wouldn’t obviate the need for such early termination. But hopefully, such architectures already have that functionality built in. It’s something to research in the new year.

  I guess there are also process limits, enforced by the shell. I don’t think I have ever gotten those to work correctly, though.