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• dnn_backend_native_layer_mathunary : add floor support

  6 août 2020, par Mingyu Yin

  dnn_backend_native_layer_mathunary : add floor support
  It can be tested with the model generated with below python script :
  import tensorflow as tf
  
  import os
  
  import numpy as np
  
  import imageio
  
  from tensorflow.python.framework import graph_util
  
  name = 'floor'
  pb_file_path = os.getcwd()
  
  if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)) :
  
      os.mkdir(pb_file_path+'/{}_savemodel/'.format(name))
  with tf.Session(graph=tf.Graph()) as sess :
  
      in_img = imageio.imread('detection.jpg')
  
      in_img = in_img.astype(np.float32)
  
      in_data = in_img[np.newaxis, :]
  
      input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
  
      y_ = tf.math.floor(input_x*255)/255
  
      y = tf.identity(y_, name='dnn_out')
  
      sess.run(tf.global_variables_initializer())
  
      constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
      with tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name), mode='wb') as f :
  
          f.write(constant_graph.SerializeToString())
      print("model.pb generated, please in ffmpeg path use\n \n \
  
      python tools/python/convert.py {}_savemodel/model.pb —outdir={}_savemodel/ \n \nto generate model.model\n".format(name,name))
      output = sess.run(y, feed_dict= input_x : in_data)
  
      imageio.imsave("out.jpg", np.squeeze(output))
      print("To verify, please ffmpeg path use\n \n \
  
      ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 {}_savemodel/tensorflow_out.md5\n  \
  
      or\n \
  
      ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow {}_savemodel/out_tensorflow.jpg\n \nto generate output result of tensorflow model\n".format(name, name, name, name))
      print("To verify, please ffmpeg path use\n \n \
  
      ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 {}_savemodel/native_out.md5\n  \
  
      or \n \
  
      ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native {}_savemodel/out_native.jpg\n \nto generate output result of native model\n".format(name, name, name, name))
  Signed-off-by : Mingyu Yin <mingyu.yin@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.c
  • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.h
  • [DH] tests/dnn/dnn-layer-mathunary-test.c
  • [DH] tools/python/convert_from_tensorflow.py
  • [DH] tools/python/convert_header.py

• Simulate packet loss in internet video streaming

  31 décembre 2015, par james

  I have encoded H.264 video using FFmpeg. I want to apply packet loss model like Gilbert Eliot model to depict video transmission over unreliable network.Like in this thread TV noise is simulated. But noise on the internet is different. So is there a way to simulate packet loss/ noise for internet video streaming using FFmpeg or MATLAB ?

• passing additional values to s3 event notification for lambda consumption

  8 septembre 2017, par user1790300

  I have to write code in react-native that allows a user to upload videos to amazon s3 to be transcoded for consumption by various devices. For the processing after the upload occurs ; I am reviewing two approaches :

  1) I can use Lambda with ffmpeg to handle the transcoding immediately after the uploading occurs (my fear here would be the amount of time required to transcode the videos and the effect on pricing if it takes a considerable amount of time).

  2) I can have s3 pass an sns message to a rest api after the created event occurs and the rest api generate a rabbitmq message that will be processed by worker that will perform the transcoding using ffmpeg.

  Option 1) seems to be the preferable option based on a completion time perspective. How concerned should I be with using 1) considering how long video transcoding might take as opposed to option 2) ?

  Also, regardless, I need a way to pass additional parameters to lambda or along the sns messaging that would allow me to somehow associate the user who uploaded the video with their account. Is there a way to pass additional text-based values to s3 to pass along to lambda or along sns when the upload completes, as a caveat I plan to upload the video directly to s3 using the rest layer(found this answer here : http://docs.aws.amazon.com/AmazonS3/latest/API/RESTObjectPUT.html#RESTObjectPUT-responses-examples) ?