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• Rails 5 with Carrierwave and S3 - creating multiple video formats for DASH streaming works but mpd file breaks

  22 novembre 2019, par Milind

  what I am doing -
  i have a Rails 5 app for video streaming(DASH MPEG) that uses FFMPEG to get encoded stream videos by converting any single video into multiple videos of multiple bit rates/size and primarily also MPD FILE that can be played easily on html video player, which i have already tested by manually running the ffmpeg scripts on the console that generates all the files.However, I want to automate this process and hence carrierwave comes into the pictures.
  Here, i use carrierwave to generate different versions(size/bitrate) of videos(mp4/webm) to upload to s3 but during running the version, where all the versions are successfully created in tmp folder, only the last version(mpd) that needs to create .mpd file, carrierwave creates a mp4 video file and just replaces the extension instead of actually creating the mpd file.

  So in the aws s3(screenshot added below), i can see my all versions and mpd file , but that mpd file which must be xml file is actually a mp4 video file or uploaded version file itself.
  I have also tried to create new file during the process, but it never works.
  Has some one encountered this problem ?

  any help will be greatly appreciated ?

  Ny code snippets below - model,uploader,output of script on the console during upload, s3 screenshot

     ##### models/video.rb ##########
  
  
  
      mount_uploader :video, VideoUploader  
  
  
  
     ####### uploaders/video_uploader.rb #########
  
  
  
      class VideoUploader < CarrierWave::Uploader::Base
  
  
  
  
  
      include CarrierWave::MiniMagick
  
      include CarrierWave::Video
  
      include CarrierWave::Video::Thumbnailer
  
      include ::CarrierWave::Backgrounder::Delay
  
  
  
      ####### for streaming ..first get the audio and then convert the input video into multiple bitrates/scale #######
  
  
  
      ###first get audio and then get all different versions of same video
  
      version :video_audio do
  
        process :get_audio
  
  
  
        def get_audio
  
          `ffmpeg -y -i "#{file.path}" -c:a aac -ac 2 -ab 128k -vn video_audio.mp4`
  
        end
  
  
  
          def full_filename(for_file)
  
            "video_audio.mp4"
  
          end
  
  
  
          def filename
  
            "video_audio.mp4"
  
          end        
  
      end 
  
  
  
      ####### similar to the above i have various version like ...#########
  
  
  
      version :video_1080 do...end
  
      version :video_720 do... end
  
      version :video_480 do ...end
  
      ...and so on..and all these versions are successfully created and uploaded to s3, however..in next version ...show it also creates a video file whereas i need a simple mpd file ONLY.
  
  
  
           ###this is where even after everything works, in S3, i can see a video file of version mpd and not actual mpd file
  
           version :mpd  do
  
             process :get_manifest
  
                ###here in the command below, the video.mpd file is successfully obtained but its uploaded as video.mpd file of added/uploaded video file and not a new mpd file
  
                ###tried with ffmpeg -f webm_dash_manifest -i too, but s3 still shows a mp4 file
  
                `MP4Box -dash 1000 -rap -frag-rap -profile onDemand -out video.mpd video_1080.mp4 video_720.mp4 video_480.mp4 video_360.mp4 video_240.mp4 video_audio.mp4 `
  
  
  
             end
  
            end
  
  
  
           ######### sidekiq console output - successful mpd is generated ################
  
                DASH-ing files - single segment
  
                Subsegment duration 1.000 - Fragment duration: 1.000 secs
  
                Splitting segments and fragments at GOP boundaries
  
                DASHing file video_1080.mp4
  
                DASHing file video_720.mp4                                  
  
                DASHing file video_480.mp4                                  
  
                DASHing file video_360.mp4                                  
  
                DASHing file video_240.mp4                                  
  
                DASHing file video_audio.mp4                                
  
               \[DASH\] Generating MPD at time 2019-11-22T00:01:59.872Z      
  
                mpd_1mb.mp4
  
                mpd_video.mpd

  this is what the uploaded files looks on s3, notice the video.mpd, its a mp4 video file just like others which should have been a simple mpd file of not more than 2kb.

  Is there something that I am missing ?
  Can Carrierwave do this or is it not made for this ?
  Do I have to write a callback and then programmatically upload files to s3, if carrierwave is not helping in this regard ?

  Kindly provide any suggestion or useful advice so that I can move ahead.

  

• avcodec/v410dec : add support for frame and slice threading

  25 novembre 2019, par Limin Wang

  avcodec/v410dec : add support for frame and slice threading
  1, Test server configure :
  
  [root@localhost ]# cat /proc/cpuinfo  |grep "model name"
  
  model name	 : Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz
  
  model name	 : Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz
  
  ...
  [root@localhost ]# free -h
  
                total        used        free      shared  buff/cache   available
  
                Mem :           102G        1.1G        100G         16M        657M        100G
  
                Swap :          4.0G          0B        4.0G
  2, Test result :
  
  encode the v410 input data for testing :
  
   ./ffmpeg -y -i 4k_422.ts  -c:v v410 -vframes 10 test.avi
  master :
  
  ./ffmpeg -y -stream_loop 1000 -i ./test.avi -benchmark -f null -
  
  frame=10010 fps= 37 q=-0.0 Lsize=N/A time=00:38:26.30 bitrate=N/A speed= 8.6x
  
  video:5240kB audio:432432kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead : unknown
  
  bench : utime=166.016s stime=102.192s rtime=268.120s
  
  bench : maxrss=273400kB
  patch applied :
  
  ./ffmpeg -y -threads 2 -thread_type slice -stream_loop 1000 -i ./test.avi -benchmark -f null -
  
  frame=10010 fps= 53 q=-0.0 Lsize=N/A time=00:38:26.30 bitrate=N/A speed=12.3x
  
  video:5240kB audio:432432kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead : unknown
  
  bench : utime=165.135s stime=100.456s rtime=187.994s
  
  bench : maxrss=275476kB
  ./ffmpeg -y -threads 2 -thread_type frame -stream_loop 1000 -i ./test.avi -benchmark -f null -
  
  frame=10010 fps= 61 q=-0.0 Lsize=N/A time=00:38:26.30 bitrate=N/A speed=14.1x
  
  video:5240kB audio:432432kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead : unknown
  
  bench : utime=171.386s stime=122.102s rtime=163.637s
  
  bench : maxrss=340308kB
  Signed-off-by : Limin Wang <lance.lmwang@gmail.com>
  
  Signed-off-by : Michael Niedermayer <michael@niedermayer.cc>
  

  • [DH] libavcodec/v410dec.c

• avfilter/vf_dnn_processing : add a generic filter for image proccessing with dnn networks

  31 octobre 2019, par Guo, Yejun

  avfilter/vf_dnn_processing : add a generic filter for image proccessing with dnn networks
  This filter accepts all the dnn networks which do image processing.
  
  Currently, frame with formats rgb24 and bgr24 are supported. Other
  
  formats such as gray and YUV will be supported next. The dnn network
  
  can accept data in float32 or uint8 format. And the dnn network can
  
  change frame size.
  The following is a python script to halve the value of the first
  
  channel of the pixel. It demos how to setup and execute dnn model
  
  with python+tensorflow. It also generates .pb file which will be
  
  used by ffmpeg.
  import tensorflow as tf
  
  import numpy as np
  
  import imageio
  
  in_img = imageio.imread('in.bmp')
  
  in_img = in_img.astype(np.float32)/255.0
  
  in_data = in_img[np.newaxis, :]
  
  filter_data = np.array([0.5, 0, 0, 0, 1., 0, 0, 0, 1.]).reshape(1,1,3,3).astype(np.float32)
  
  filter = tf.Variable(filter_data)
  
  x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
  
  y = tf.nn.conv2d(x, filter, strides=[1, 1, 1, 1], padding='VALID', name='dnn_out')
  
  sess=tf.Session()
  
  sess.run(tf.global_variables_initializer())
  
  output = sess.run(y, feed_dict=x : in_data)
  
  graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
  
  tf.train.write_graph(graph_def, '.', 'halve_first_channel.pb', as_text=False)
  
  output = output * 255.0
  
  output = output.astype(np.uint8)
  
  imageio.imsave("out.bmp", np.squeeze(output))
  To do the same thing with ffmpeg :
  
   generate halve_first_channel.pb with the above script
  
   generate halve_first_channel.model with tools/python/convert.py
  
   try with following commands
  
    ./ffmpeg -i input.jpg -vf dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_out:fmt=rgb24:dnn_backend=native -y out.native.png
  
    ./ffmpeg -i input.jpg -vf dnn_processing=model=halve_first_channel.pb:input=dnn_in:output=dnn_out:fmt=rgb24:dnn_backend=tensorflow -y out.tf.png
  Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
  
  Signed-off-by : Pedro Arthur <bygrandao@gmail.com>
  

  • [DH] configure
  • [DH] doc/filters.texi
  • [DH] libavfilter/Makefile
  • [DH] libavfilter/allfilters.c
  • [DH] libavfilter/vf_dnn_processing.c