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• Monster Battery Power Revisited

  28 mai 2010, par Multimedia Mike — Python, Science Projects

  So I have this new fat netbook battery and I performed an experiment to determine how long it really lasts. In my last post on the matter, it was suggested that I should rely on the information that gnome-power-manager is giving me. However, I have rarely seen GPM report more than about 2 hours of charge ; even on a full battery, it only reports 3h25m when I profiled it as lasting over 5 hours in my typical use. So I started digging to understand how GPM gets its numbers and determine if, perhaps, it’s not getting accurate data from the system.

  I started poking around /proc for the data I wanted. You can learn a lot in /proc as long as you know the right question to ask. I had to remember what the power subsystem is called — ACPI — and this led me to /proc/acpi/battery/BAT0/state which has data such as :

  present :                 yes
  capacity state :          ok
  charging state :          charged
  present rate :            unknown
  remaining capacity :      100 mAh
  present voltage :         8326 mV
  

  "Remaining capacity" rated in mAh is a little odd ; I would later determine that this should actually be expressed as a percentage (i.e., 100% charge at the time of this reading). Examining the GPM source code, it seems to determine as a function of the current CPU load (queried via /proc/stat) and the battery state queried via a facility called devicekit. I couldn’t immediately find any source code to the latter but I was able to install a utility called ’devkit-power’. Mostly, it appears to rehash data already found in the above /proc file.

  Curiously, the file /proc/acpi/battery/BAT0/info, which displays essential information about the battery, reports the design capacity of my battery as only 4400 mAh which is true for the original battery ; the new monster battery is supposed to be 10400 mAh. I can imagine that all of these data points could be conspiring to under-report my remaining battery life.

  Science project : Repeat the previous power-related science project but also parse and track the remaining capacity and present voltage fields from the battery state proc file.

  Let’s skip straight to the results (which are consistent with my last set of results in terms of longevity) :

  
  
  

  So there is definitely something strange going on with the reporting— the 4400 mAh battery reports discharge at a linear rate while the 10400 mAh battery reports precipitous dropoff after 60%.

  Another curious item is that my script broke at first when there was 20% power remaining which, as you can imagine, is a really annoying time to discover such a bug. At that point, the "time to empty" reported by devkit-power jumped from 0 seconds to 20 hours (the first state change observed for that field).

  Here’s my script, this time elevated from Bash script to Python. It requires xdotool and devkit-power to be installed (both should be available in the package manager for a distro).
  

  PLAIN TEXT

  PYTHON :

  1. # !/usr/bin/python
  2.  
  3. import commands
  4. import random
  5. import sys
  6. import time
  7.  
  8. XDOTOOL = "/usr/bin/xdotool"
  9. BATTERY_STATE = "/proc/acpi/battery/BAT0/state"
  10. DEVKIT_POWER = "/usr/bin/devkit-power -i /org/freedesktop/DeviceKit/Power/devices/battery_BAT0"
  11.  
  12. print "count, unixtime, proc_remaining_capacity, proc_present_voltage, devkit_percentage, devkit_voltage"
  13.  
  14. count = 0
  15. while 1 :
  16.   commands.getstatusoutput("%s mousemove %d %d" % (XDOTOOL, random.randrange(0,800), random.randrange(0, 480)))
  17.   battery_state = open(BATTERY_STATE).read().splitlines()
  18.   for line in battery_state :
  19.     if line.startswith("remaining capacity :") :
  20.       proc_remaining_capacity = int(line.lstrip("remaining capacity : ").rstrip("mAh"))
  21.     elif line.startswith("present voltage :") :
  22.       proc_present_voltage = int(line.lstrip("present voltage : ").rstrip("mV"))
  23.   devkit_state = commands.getoutput(DEVKIT_POWER).splitlines()
  24.   for line in devkit_state :
  25.     line = line.strip()
  26.     if line.startswith("percentage :") :
  27.       devkit_percentage = int(line.lstrip("percentage :").rstrip(’\%’))
  28.     elif line.startswith("voltage :") :
  29.       devkit_voltage = float(line.lstrip("voltage :").rstrip(’V’)) * 1000
  30.   print "%d, %d, %d, %d, %d, %d" % (count, time.time(), proc_remaining_capacity, proc_present_voltage, devkit_percentage, devkit_voltage)
  31.   sys.stdout.flush()
  32.   time.sleep(60)
  33.   count += 1

• FFmpeg and Code Coverage Tools

  21 août 2010, par Multimedia Mike — FATE Server, Python

  Code coverage tools likely occupy the same niche as profiling tools : Tools that you’re supposed to use somewhere during the software engineering process but probably never quite get around to it, usually because you’re too busy adding features or fixing bugs. But there may come a day when you wish to learn how much of your code is actually being exercised in normal production use. For example, the team charged with continuously testing the FFmpeg project, would be curious to know how much code is being exercised, especially since many of the FATE test specs explicitly claim to be "exercising XYZ subsystem".

  The primary GNU code coverage tool is called gcov and is probably already on your GNU-based development system. I set out to determine how much FFmpeg source code is exercised while running the full FATE suite. I ran into some problems when trying to use gcov on a project-wide scale. I spackled around those holes with some very ad-hoc solutions. I’m sure I was just overlooking some more obvious solutions about which you all will be happy to enlighten me.

  Results
  I’ve learned to cut to the chase earlier in blog posts (results first, methods second). With that, here are the results I produced from this experiment. This Google spreadsheet contains 3 sheets : The first contains code coverage stats for a bunch of FFmpeg C files sorted first by percent coverage (ascending), then by number of lines (descending), thus highlighting which files have the most uncovered code (ffserver.c currently tops that chart). The second sheet has files for which no stats were generated. The third sheet has "problems". These files were rejected by my ad-hoc script.

  Here’s a link to the data in CSV if you want to play with it yourself.

  Using gcov with FFmpeg
  To instrument a program for gcov analysis, compile and link the target program with the -fprofile-arcs and -ftest-coverage options. These need to be applied at both the compile and link stages, so in the case of FFmpeg, configure with :

    ./configure \
      —extra-cflags="-fprofile-arcs -ftest-coverage" \
      —extra-ldflags="-fprofile-arcs -ftest-coverage"
  

  The building process results in a bunch of .gcno files which pertain to code coverage. After running the program as normal, a bunch of .gcda files are generated. To get coverage statistics from these files, run 'gcov sourcefile.c'. This will print some basic statistics as well as generate a corresponding .gcov file with more detailed information about exactly which lines have been executed, and how many times.

  Be advised that the source file must either live in the same directory from which gcov is invoked, or else the path to the source must be given to gcov via the '-o, --object-directory' option.

  Resetting Statistics
  Statistics in the .gcda are cumulative. Should you wish to reset the statistics, doing this in the build directory should suffice :

    find . -name "*.gcda" | xargs rm -f
  

  Getting Project-Wide Data
  As mentioned, I had to get a little creative here to get a big picture of FFmpeg code coverage. After building FFmpeg with the code coverage options and running FATE,

  for file in `find . -name "*.c"` \
  do \
    echo "*****" $file \
    gcov -o `dirname $file` `basename $file` \
  done > ffmpeg-code-coverage.txt 2>&1
  

  After that, I ran the ffmpeg-code-coverage.txt file through a custom Python script to print out the 3 CSV files that I later dumped into the Google Spreadsheet.

  Further Work
  I’m sure there are better ways to do this, and I’m sure you all will let me know what they are. But I have to get the ball rolling somehow.

  There’s also TestCocoon. I’d like to try that program and see if it addresses some of gcov’s shortcomings (assuming they are indeed shortcomings rather than oversights).

  Source for script : process-gcov-slop.py

  PLAIN TEXT

  PYTHON :

  1. # !/usr/bin/python
  2.  
  3. import re
  4.  
  5. lines = open("ffmpeg-code-coverage.txt").read().splitlines()
  6. no_coverage = ""
  7. coverage = "filename, % covered, total lines\n"
  8. problems = ""
  9.  
  10. stats_exp = re.compile(’Lines executed :(\d+\.\d+)% of (\d+)’)
  11. for i in xrange(len(lines)) :
  12.   line = lines[i]
  13.   if line.startswith("***** ") :
  14.     filename = line[line.find(’./’)+2 :]
  15.     i += 1
  16.     if lines[i].find(":cannot open graph file") != -1 :
  17.       no_coverage += filename + ’\n’
  18.     else :
  19.       while lines[i].find(filename) == -1 and not lines[i].startswith("***** ") :
  20.         i += 1
  21.       try :
  22.         (percent, total_lines) = stats_exp.findall(lines[i+1])[0]
  23.         coverage += filename + ’, ’ + percent + ’, ’ + total_lines + ’\n’
  24.       except IndexError :
  25.         problems += filename + ’\n’
  26.  
  27. open("no_coverage.csv", ’w’).write(no_coverage)
  28. open("coverage.csv", ’w’).write(coverage)
  29. open("problems.csv", ’w’).write(problems)

• How to HLS-live-stream incoming batches of individual frames, "appending" to a m3u8 playlist in real time, with ffmpeg ?

  20 novembre 2024, par Rob

  My overall goal :

  



  Server-side :

  



  

  • I have batches of sequential, JPEG-encoded frames (8-16) arriving from time to time, generated at roughly 2 FPS.

  


  • I would like to host an HLS live stream, where, when a new batch of frames arrives, I encode those new frames as h264 .ts segments with ffmpeg, and have the new .ts segments automatically added to an HLS stream (e.g. .m3u8 file).

  


  



  Client/browser-side :

  



  

  • When the .m3u8 is updated, I would like the video stream being watched to simply "continue", advancing from the point where new .ts segments have been added.

  


  • I do not need the user to scrub backwards in time, the client just needs to support live observation of the stream.

  


  



  



  My current approach :

  



  Server-side :

  



  To generate the "first" few segments of the stream, I'm attempting the below (just command-line for now to get ffmpeg working right, but ultimately will be automated via a Python script) :

  



  For reference, I'm using ffmpeg version 3.4.6-0ubuntu0.18.04.1.

  



  ffmpeg -y -framerate 2 -i /frames/batch1/frame_%d.jpg \
       -c:v libx264 -crf 21 -preset veryfast -g 2 \
       -f hls -hls_time 4 -hls_list_size 4 -segment_wrap 4 -segment_list_flags +live video/stream.m3u8


  



  where the /frames/batch1/ folder contains a sequence of frames (e.g. frame_01.jpg, frame_02.jpg, etc...). This already doesn't appear to work correctly, because it keeps adding #EXT-X-ENDLIST to the end of the .m3u8 file, which as I understand is not correct for a live HLS stream - here's what that generates :

  



  #EXTM3U
#EXT-X-VERSION:3
#EXT-X-TARGETDURATION:4
#EXT-X-MEDIA-SEQUENCE:0
#EXTINF:4.000000,
stream0.ts
#EXTINF:4.000000,
stream1.ts
#EXTINF:2.000000,
stream2.ts
#EXT-X-ENDLIST


  



  I can't figure out how to suppress #EXT-X-ENDLIST here - this is problem #1.

  



  Then, to generate subsequent segments (e.g. when new frames become available), I'm trying this :

  



  ffmpeg -y -framerate 2 -start_number 20 -i /frames/batch2/frame_%d.jpg \
       -c:v libx264 -crf 21 -preset veryfast -g 2 \
       -f hls -hls_time 4 -hls_list_size 4 -segment_wrap 4 -segment_list_flags +live video/stream.m3u8


  



  Unfortunately, this does not work the way I want it to. It simply overwrites stream.m3u8, does and does not advance #EXT-X-MEDIA-SEQUENCE, it does not index the new .ts files correctly, and it also includes the undesirable #EXT-X-ENDLIST - this is the output of that command :

  



  #EXTM3U
#EXT-X-VERSION:3
#EXT-X-TARGETDURATION:4
#EXT-X-MEDIA-SEQUENCE:0
#EXTINF:4.000000,
stream0.ts
#EXTINF:4.000000,
stream1.ts
#EXTINF:3.000000,
stream2.ts
#EXT-X-ENDLIST


  



  Fundamentally, I can't figure out how to "append" to an existing .m3u8 in a way that makes sense for HLS live streaming. That's essentially problem #2.

  



  For hosting the stream, I'm using a simple Flask app - which appears to be working the way I intend - here's what I'm doing for reference :

  



  @app.route('/video/')
def stream(file_name):
    video_dir = './video'
    return send_from_directory(directory=video_dir, filename=file_name)


  



  Client-side :

  



  I'm trying HLS.js in Chrome - basically boils down to this :

  



  <video></video>&#xA;&#xA;...&#xA;&#xA;<code class="echappe-js">&lt;script src=&quot;https://cdn.jsdelivr.net/npm/hls.js@latest&quot;&gt;&lt;/script&gt;&#xA;&lt;script&gt;&amp;#xA;   var video = document.getElementById(&amp;#x27;video1&amp;#x27;);&amp;#xA;   if (Hls.isSupported()) {&amp;#xA;     var hls = new Hls();&amp;#xA;     hls.loadSource(&amp;#x27;/video/stream.m3u8&amp;#x27;);&amp;#xA;     hls.attachMedia(video);&amp;#xA;     hls.on(Hls.Events.MANIFEST_PARSED, function() {&amp;#xA;       video.play();&amp;#xA;     });&amp;#xA;   }&amp;#xA;   else if (video.canPlayType(&amp;#x27;application/vnd.apple.mpegurl&amp;#x27;)) {&amp;#xA;     video.src = &amp;#x27;/video/stream.m3u8&amp;#x27;;&amp;#xA;     video.addEventListener(&amp;#x27;loadedmetadata&amp;#x27;, function() {&amp;#xA;       video.play();&amp;#xA;     });&amp;#xA;   }&amp;#xA;&lt;/script&gt;   &#xA;

  



  I'd like to think that what I'm trying to do doesn't require a more complex approach than what I'm trying above, but since what I'm trying to far definitely isn't working, I'm starting to think I need to come at this from a different angle. Any ideas on what I'm missing ?

  



  Edit :

  



  I've also attempted the same (again in Chrome) with video.js, and am seeing similar behavior - in particular, when I manually update the backing stream.m3u8 (with no #EXT-X-ENDLIST tag), videojs never picks up the new changes to the live stream, and just buffers/hangs indefinitely.

  



  <video class="video-js vjs-default-skin" muted="muted" controls="controls">&#xA;    <source type="application/x-mpegURL" src="/video/stream.m3u8">&#xA;</source></video>&#xA;&#xA;...&#xA;&#xA;<code class="echappe-js">&lt;script&gt;&amp;#xA;    var player = videojs(&amp;#x27;video1&amp;#x27;);&amp;#xA;    player.play();&amp;#xA;&lt;/script&gt;&#xA;

  



  For example, if I start with this initial version of stream.m3u8 :

  



  #EXTM3U
#EXT-X-PLAYLIST-TYPE:EVENT
#EXT-X-VERSION:3
#EXT-X-TARGETDURATION:8
#EXT-X-MEDIA-SEQUENCE:0
#EXTINF:4.000000,
stream0.ts
#EXTINF:4.000000,
stream1.ts
#EXTINF:2.000000,
stream2.ts


  



  and then manually update it server-side to this :

  



  #EXTM3U
#EXT-X-PLAYLIST-TYPE:EVENT
#EXT-X-VERSION:3
#EXT-X-TARGETDURATION:8
#EXT-X-MEDIA-SEQUENCE:3
#EXTINF:4.000000,
stream3.ts
#EXTINF:4.000000,
stream4.ts
#EXTINF:3.000000,
stream5.ts


  



  the video.js control just buffers indefinitely after only playing the first 3 segments (stream*.ts 0-2), which isn't what I'd expect to happen (I'd expect it to continue playing stream*.ts 3-5 once stream.m3u8 is updated and video.js makes a request for the latest version of the playlist).