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• Video File Size Optimization

  14 mars 2020, par Heba Gamal Eldin

  I’m trying the 2-pass technique of the FFmpeg in python but couldn’t find any python tutorials do this task.
  is there is no way instead of using Subprocess ? if there’s any illustrative example please provide me.

  Note :

  I have tried the 2-pass in the script like that :

  input_fit  = {self.video_in:None}
  
  output = {None:"-c:v h264 -b:v 260k -pass 1 -an -f mp4 NUL && ^",
  
            self.video_out:("ffmpeg -i \"%s\" -c:v h264 -b:v 260k -pass 2 " %self.video_in)}
  
           ## video_out IS The Name of The output File ##
  
  model = FFmpeg(inputs = input_fit, outputs= output)
  
  print(model.cmd)

  It Raises an error of

  : : FFRuntimeError : exited with status 1,

  but when i take the generated command and run it on the ffmpeg cmd it runs without errors and generates the video perfectly.
  so anyone just could tell me what is the problem please ?

• Is There ANYWAY to see the x_test data and labels after the train test split functions operations

  10 février 2023, par MatPar

  I am have been searching google etc for a solution to this challenge for a few weeks now.

  


  What is it ?
I am trying to visualise the data that is being used in the XTEST Variable via the split() function below.

  


  Either via text/string output or the actual image that is being used therein that variable at that given time. Both would be very helpful.

  


  For now I am using 80 videos in a Training 80 : Testing 20 split, where the validation is taking 20% of Training.

  


  I selected various types of data for the training to see how well the model is at predicting the outcome.

  


  So in the end I have just 16 videos for Testing for now.

  


  WHAT I AM TRYING TO SOLVE : IS ==> what those videos are ?!!!

  


  I have no way of knowing what selection of videos were chosen in that group of 16 for processing.

  


  To solve this, I am trying to pass in the video label so that it can present an ID of the specific selection within the XTEST data variable.

  


  WHY I AM DOING THIS
The model is being challenge by a selection of videos that I have no control over.
If I can identify these videos, I can analyse the data and enhance the model's performance accordingly.

  


  The confusion matrix is doing the same, it is presenting me with just 4 misclassifications but what those 4 misclassifications are ? I have no clue.

  


  That is not a good approach, hence me asking these questions.

  


  ** THE CODE ** where I am at

  


  X_train, Xtest, Y_train, Ytest = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=1, stratify=Y, shuffle=True)
#print(Xtest)

history = model.fit(X_train, Y_train, validation_split=0.20, batch_size=args.batch,epochs=args.epoch, verbose=1, callbacks=[rlronp], shuffle=True)

predict_labels = model.predict(Xtest, batch_size=args.batch,verbose=1,steps=None,callbacks=None,max_queue_size=10,workers=1,use_multiprocessing=False)
print('This is prediction labels',predict_labels)# This has no video label indentifiers  


  


  This is working fine, but I cannot draw a hypothesis until I see what's within the Xtest variable.

  


  All I am getting is an array of data with no labels.

  


  For example : Xtest has 16 videos after the split operations :

  


  is it vid04.mp4, vid34.mp4, vid21.mp4, vid34.mp4, vid74.mp4, vid54.mp4, vid71.mp4, vid40.mp4, vid06.mp4, vid27.mp4, vid32.mp4, vid18.mp4, vid66.mp4, vid42.mp4, vid8.mp4, vid14.mp4, etc ???!?!??!?!

  


  This is what I really want to see !!!

  


  Please assist me to understand the process and where I am going wrong..
Thanx in advance for acknowledging my challenge !

  


• vf_dnn_processing.c : add dnn backend openvino

  25 mai 2020, par Guo, Yejun

  vf_dnn_processing.c : add dnn backend openvino
  We can try with the srcnn model from sr filter.
  
  1) get srcnn.pb model file, see filter sr
  
  2) convert srcnn.pb into openvino model with command :
  
  python mo_tf.py —input_model srcnn.pb —data_type=FP32 —input_shape [1,960,1440,1] —keep_shape_ops
  See the script at https://github.com/openvinotoolkit/openvino/tree/master/model-optimizer
  
  We'll see srcnn.xml and srcnn.bin at current path, copy them to the
  
  directory where ffmpeg is.
  I have also uploaded the model files at https://github.com/guoyejun/dnn_processing/tree/master/models
  3) run with openvino backend :
  
  ffmpeg -i input.jpg -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=openvino:model=srcnn.xml:input=x:output=srcnn/Maximum -y srcnn.ov.jpg
  
  (The input.jpg resolution is 720*480)
  Also copy the logs on my skylake machine (4 cpus) locally with openvino backend
  
  and tensorflow backend. just for your information.
  $ time ./ffmpeg -i 480p.mp4 -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=tensorflow:model=srcnn.pb:input=x:output=y -y srcnn.tf.mp4
  
  …
  
  frame=  343 fps=2.1 q=31.0 Lsize=    2172kB time=00:00:11.76 bitrate=1511.9kbits/s speed=0.0706x
  
  video:1973kB audio:187kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead : 0.517637%
  
  [aac @ 0x2f5db80] Qavg : 454.353
  
  real    2m46.781s
  
  user    9m48.590s
  
  sys     0m55.290s
  $ time ./ffmpeg -i 480p.mp4 -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=openvino:model=srcnn.xml:input=x:output=srcnn/Maximum -y srcnn.ov.mp4
  
  …
  
  frame=  343 fps=4.0 q=31.0 Lsize=    2172kB time=00:00:11.76 bitrate=1511.9kbits/s speed=0.137x
  
  video:1973kB audio:187kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead : 0.517640%
  
  [aac @ 0x31a9040] Qavg : 454.353
  
  real    1m25.882s
  
  user    5m27.004s
  
  sys     0m0.640s
  Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
  
  Signed-off-by : Pedro Arthur <bygrandao@gmail.com>
  

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