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• FFServer streaming H.264 from Logitech C920 without re-encoding

  29 novembre 2016, par Zoltan Fedor

  I’m trying to broadcast a native .H264 webcam feed from a Logitech C920 webcam in realtime from an Odroid device (a robot) via ffserver running on a separate server (CentOS 7.1) to users’ browser without reeconding the .H264 video feed.

  Having a realtime video feed in the browser is a challenge on its own, so for now I’m just trying to get the Logitech C920 webcam on the Odroid to stream its native .H264 realtime video feed as mp4 via ffserver to users without the need to reencode the video in the process.
  Obviously I want to avoid re-encoding as that would take too much CPU time and would kill the realtime video feed. Later I might need to change the container to .flv or rtp, so it can be played from the browser in a realtime fashion. I’m using the Logitech C920 webcam, because it can do .H264 encoding on the hardware. (it has been tested by saving a file directly, it works, except the well-known ’jerkiness’ issue related to a linux kernel bug : http://sourceforge.net/p/linux-uvc/mailman/message/33164469/ , but that is a different story)

  The problem is, that however I set ffmpeg-ffserver up, as soon as ffserver is in the picture the feed gets reencoded - even from h264(native) to h264(libx264) - taking up 100% of CPU on the Odroid device and introducing a huge delay in the video feed.

  Below are my ffmpeg and ffserver settings.

  Ffmpeg from the Odroid device streaming the .H264 feed to ffserver

  $ ffmpeg -s 1920x1080 -f v4l2 -vcodec h264 -i /dev/video0 -copyinkf -vcodec copy http://xxxyyyy.com:8090/feed1.ffm
  
  ffmpeg version N-72744-g653bf3c Copyright (c) 2000-2015 the FFmpeg developers
  
    built with gcc 4.8 (Ubuntu/Linaro 4.8.2-19ubuntu1)
  
    configuration: --prefix=/home/odroid/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/odroid/ffmpeg_build/include --extra-ldflags=-L/home/odroid/ffmpeg_build/lib --bindir=/home/odroid/bin --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libtheora --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-nonfree
  
    libavutil      54. 27.100 / 54. 27.100
  
    libavcodec     56. 41.100 / 56. 41.100
  
    libavformat    56. 36.100 / 56. 36.100
  
    libavdevice    56.  4.100 / 56.  4.100
  
    libavfilter     5. 16.101 /  5. 16.101
  
    libswscale      3.  1.101 /  3.  1.101
  
    libswresample   1.  2.100 /  1.  2.100
  
    libpostproc    53.  3.100 / 53.  3.100
  
  Input #0, video4linux2,v4l2, from '/dev/video0':
  
    Duration: N/A, start: 6581.606726, bitrate: N/A
  
      Stream #0:0: Video: h264 (Constrained Baseline), yuvj420p(pc), 1920x1080 [SAR 1:1 DAR 16:9], -5 kb/s, 30 fps, 30 tbr, 1000k tbn, 60 tbc
  
  [swscaler @ 0x11bf0b0] deprecated pixel format used, make sure you did set range correctly
  
  No pixel format specified, yuvj420p for H.264 encoding chosen.
  
  Use -pix_fmt yuv420p for compatibility with outdated media players.
  
  [libx264 @ 0x12590e0] using SAR=64/45
  
  [libx264 @ 0x12590e0] using cpu capabilities: ARMv6 NEON
  
  [libx264 @ 0x12590e0] profile High, level 1b
  
  Output #0, ffm, to 'http://robo-car.int.thomsonreuters.com:8090/feed1.ffm':
  
    Metadata:
  
      creation_time   : now
  
      encoder         : Lavf56.36.100
  
      Stream #0:0: Video: h264 (libx264), yuvj420p(pc), 160x128 [SAR 64:45 DAR 16:9], q=-1--1, 64 kb/s, 30 fps, 1000k tbn, 5 tbc
  
      Metadata:
  
        encoder         : Lavc56.41.100 libx264
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
  
  Press [q] to stop, [?] for help
  
  ^Cav_interleaved_write_frame(): Immediate exit requested00 bitrate=N/A dup=0 drop=97    
  
      Last message repeated 2140 times
  
  frame= 3723 fps=301 q=-1.0 Lsize=     396kB time=00:12:14.20 bitrate=   4.4kbits/s dup=3699 drop=103    
  
  video:321kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 23.500496%

  And the /etc/ffserver.conf on the server running ffserver :

  HTTPPort 8090                      # Port to bind the server to
  
  HTTPBindAddress 0.0.0.0
  
  MaxHTTPConnections 2000
  
  MaxClients 1000
  
  MaxBandwidth 10000             # Maximum bandwidth per client
  
                                 # set this high enough to exceed stream bitrate
  
  CustomLog -
  
  
  
  <feed>         # This is the input feed where FFmpeg will send
  
     File ./feed1.ffm            # video stream.
  
     FileMaxSize 1G              # Maximum file size for buffering video
  
  </feed>
  
  
  
  <stream>
  
    Feed feed1.ffm
  
    Format mp4
  
    NoAudio
  
  </stream>

  As you have seen above in the ffmpeg section, there is a reencoding happening on the Odroid device maxing out the CPUs :

  Stream mapping:
  
    Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))

  I have already tried setting the VideoCodec value in the ffserver config directly to libx264, tried the -re setting in ffmpeg, tried using different syntax for ffmpeg, etc. Nothing helps. Reeconding is always there and so I can’t make ffmpeg-ffserver just to broadcast the video stream as-is.

  Both ffmpeg (on the Odroid and on the server) were compiled yesterday (2015-06-09) from source, so they are the latest (and the same) version.

  Any idea ?

  EDIT :
  IN SUMMARY the issue is : I cannot find a way to get ffserver to broadcast the h264(native) feed coming from the Logitech C920 webcam without re-encoding.

• compiling ffmpeg and chromaprint for android with success on some devices

  4 août 2016, par D Liebman

  I am following the recommendations of this site :

  https://bitbucket.org/phorton1/chromaprint

  From the site I have copied the multi-configure and multi-make scripts for the ffmpeg and chromaprint libraries. I have used them with recent clones of ffmpeg 3.0 and chromaprint 1.3.2 . I get working ’.so’ files for arm, x86 and armv7. I don’t have compillation problems. I load them into my app (a very simple app just to test this library) and I try it on my two devices. One is a Samsung Galaxy TabPro (SM-T320, api 19), and one is the Samsung S4 (api 21). I get the program to run on both. I can use the ’-version’ command to get the chromaprint version info. When I try to fingerprint a audio file, the software works on the older tablet, but not on the s4. On the s4 the error message is ’error decoding audio’.

  I have tried ffmpeg 0.9 as the bitbucket site suggests. I have the same problem. I have tried ffmpeg 2.7 and I thought this was working. Now I cannot get 2.7 to work. As before the code works on the api 19 device and not the s4.

  The Tab is an armeabi-v7a device and the s4 is an armeabi/armeabi-v7 device, so I thought by removing the ’armeabi’ binary I would have the same file on both machines, and they would have to work. This attempt did nothing. I am using android studio.

  Below is a list of some of my ffmpeg configurations. They are taken from the multi-configure script.

      --enable-static
  
      --disable-shared
  
      --disable-doc
  
      --disable-txtpages
  
      --disable-iconv
  
  
  
      --enable-memalign-hack
  
      --enable-debug
  
      --disable-avdevice
  
      --disable-avfilter
  
      --disable-swscale
  
      --disable-ffmpeg
  
      --disable-ffplay
  
      --disable-ffserver
  
      --disable-network
  
      --disable-muxers
  
      --disable-demuxers
  
      --enable-rdft
  
      --enable-demuxer=aac
  
      --enable-demuxer=ac3
  
      --enable-demuxer=ape
  
      --enable-demuxer=asf
  
      --enable-demuxer=flac
  
      --enable-demuxer=matroska_audio
  
      --enable-demuxer=mp3
  
      --enable-demuxer=mpc
  
      --enable-demuxer=mov
  
      --enable-demuxer=mpc8
  
      --enable-demuxer=ogg
  
      --enable-demuxer=tta
  
      --enable-demuxer=wav
  
      --enable-demuxer=wv
  
      --disable-bsfs
  
      --disable-filters
  
      --disable-parsers
  
      --enable-parser=aac
  
      --enable-parser=ac3
  
      --enable-parser=mpegaudio
  
      --disable-protocols
  
      --enable-protocol=file
  
      --disable-indevs
  
      --disable-outdevs
  
      --disable-encoders
  
      --disable-decoders
  
      --enable-decoder=aac
  
      --enable-decoder=ac3
  
      --enable-decoder=alac
  
      --enable-decoder=ape
  
      --enable-decoder=flac
  
      --enable-decoder=mp1
  
      --enable-decoder=mp2
  
      --enable-decoder=mp3
  
      --enable-decoder=mpc7
  
      --enable-decoder=mpc8
  
      --enable-decoder=tta
  
      --enable-decoder=vorbis
  
      --enable-decoder=wavpack
  
      --enable-decoder=wmav1
  
      --enable-decoder=wmav2
  
      --enable-decoder=pcm_alaw
  
      --enable-decoder=pcm_dvd
  
      --enable-decoder=pcm_f32be
  
      --enable-decoder=pcm_f32le
  
      --enable-decoder=pcm_f64be
  
      --enable-decoder=pcm_f64le
  
      --enable-decoder=pcm_s16be
  
      --enable-decoder=pcm_s16le
  
      --enable-decoder=pcm_s16le_planar
  
      --enable-decoder=pcm_s24be
  
      --enable-decoder=pcm_daud
  
      --enable-decoder=pcm_s24le
  
      --enable-decoder=pcm_s32be
  
      --enable-decoder=pcm_s32le
  
      --enable-decoder=pcm_s8
  
      --enable-decoder=pcm_u16be
  
      --enable-decoder=pcm_u16le
  
      --enable-decoder=pcm_u24be
  
      --enable-decoder=pcm_u24le
  
      --enable-decoder=rawvideo

  Here is some information about the s4 that I got from a system info app.

  Build Info
  
      Build.BOARD: MSM8960
  
      Build.BRAND: Verizon
  
      Build.DEVICE: jfltevzw
  
      Build.DISPLAY: LRX22C.I545VRUGOF1
  
      Build.PRODUCT: jfltevzw
  
      Build.HARDWARE: qcom
  
      Build.FINGERPRINT: Verizon/jfltevzw/jfltevzw:5.0.1/LRX22C/I545VRUGOF1:user/release-keys
  
      Build.CPU_ABI: armeabi-v7a
  
      Build.SUPPORTED_32_BIT_ABIS: [armeabi-v7a, armeabi]
  
      Build.SUPPORTED_64_BIT_ABIS: []
  
      Build.SUPPORTED_ABIS: [armeabi-v7a, armeabi]
  
      Build.HOST: SWDD6122
  
      Build.ID: LRX22C
  
      Build.MANUFACTURER: samsung
  
      Build.MODEL: SCH-I545
  
      Build.PRODUCT: jfltevzw
  
      Build.TAGS: release-keys
  
      Build.TIME: 2015-07-15T03:39:01.000-0400
  
      Build.TYPE: user
  
      Build.USER: dpi
  
      Build.BOOTLOADER: I545VRUGOF1
  
      Build.TAGS: release-keys
  
      Build.VERSION.CODENAME: REL
  
      Build.VERSION.INCREMENTAL: I545VRUGOF1
  
      Build.VERSION.RELEASE: 5.0.1
  
      Build.VERSION.SDK_INT: 21

  EDIT :

  This is the complete source for my project minus the api-key which is personal. https://github.com/radiodee1/awesome-fpcalc The code is arranged in parts inside the git repository. The ’fpcalc-testapp’ part just has the Android Studio project. The ’fpcalc-build’ part is my attempt to use the latest versions of ffmpeg, chromaprint, and kissfft. The ’fpcalc-build-complete’ part is a series of scripts that reproduces the P. Horton ffmpeg 2.7 scenario. Bye the way I believe the best libraries are produced in the ’build-complete’ folders.

• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft — Community, Plugins

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Piwik shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Piwik marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Piwik only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Piwik shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Piwik is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Piwik does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution