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• dnn_backend_native_layer_mathunary : add asin support

  18 juin 2020, par Ting Fu

  dnn_backend_native_layer_mathunary : add asin support
  It can be tested with the model generated with below python script :
  import tensorflow as tf
  
  import numpy as np
  
  import imageio
  in_img = imageio.imread('input.jpeg')
  
  in_img = in_img.astype(np.float32)/255.0
  
  in_data = in_img[np.newaxis, :]
  x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
  
  x1 = tf.asin(x)
  
  x2 = tf.divide(x1, 3.1416/2) # pi/2
  
  y = tf.identity(x2, name='dnn_out')
  sess=tf.Session()
  
  sess.run(tf.global_variables_initializer())
  graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
  
  tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False)
  print("image_process.pb generated, please use \
  
  path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")
  output = sess.run(y, feed_dict=x : in_data)
  
  imageio.imsave("out.jpg", np.squeeze(output))
  Signed-off-by : Ting Fu <ting.fu@intel.com>
  
  Signed-off-by : Guo Yejun <yejun.guo@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.c
  • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.h
  • [DH] tools/python/convert_from_tensorflow.py
  • [DH] tools/python/convert_header.py

• merge/combine two FFMPEG commands together into one command

  26 février 2021, par Mayank Thapliyal

  I have been processing videos for a while and I have been using ffmpeg to make my life easy. But there are two commands which I want to combine into single command :-

  


  Step 1 :- Divide a video vertically into two parts and then stack them horizontally

  


  ffmpeg -i usa.mp4 -filter_complex "[0]crop=iw:ih/2:0:0[top];[0]crop=iw:ih/2:0:oh[bottom];[top][bottom]hstack" -preset fast -c:a copy usa$.mp4

  


  Step 2 :- Combine 3 videos into single video (the video from Step 1 will be in between the start.mp4 and end.mp4)

  


  ffmpeg -i start.mp4 -i usa$.mp4 -i end.mp4 -vsync 2 -filter_complex "[0:v] [0:a] [1:v] [1:a] [2:v] [2:a] concat=n=3:v=1:a=1 [v] [a]" -map "[v]" -map "[a]" usa_.mp4

  


  Can anyone please combine the videos into single command.I will be then able to save a lot of computing time(I guess that)

  


  Thanks in advance

  


• dnn_backend_native_layer_mathunary : add atanh support

  29 juin 2020, par Ting Fu

  dnn_backend_native_layer_mathunary : add atanh support
  It can be tested with the model generated with below python script :
  import tensorflow as tf
  
  import numpy as np
  
  import imageio
  in_img = imageio.imread('input.jpeg')
  
  in_img = in_img.astype(np.float32)/255.0
  
  in_data = in_img[np.newaxis, :]
  x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
  please uncomment the part you want to test
  x_sinh_1 = tf.sinh(x)
  
  x_out = tf.divide(x_sinh_1, 1.176) # sinh(1.0)
  x_cosh_1 = tf.cosh(x)
  
  x_out = tf.divide(x_cosh_1, 1.55) # cosh(1.0)
  x_tanh_1 = tf.tanh(x)
  
  x__out = tf.divide(x_tanh_1, 0.77) # tanh(1.0)
  x_asinh_1 = tf.asinh(x)
  
  x_out = tf.divide(x_asinh_1, 0.89) # asinh(1.0/1.1)
  x_acosh_1 = tf.add(x, 1.1)
  
  x_acosh_2 = tf.acosh(x_acosh_1) # accept (1, inf)
  
  x_out = tf.divide(x_acosh_2, 1.4) # acosh(2.1)
  x_atanh_1 = tf.divide(x, 1.1)
  
  x_atanh_2 = tf.atanh(x_atanh_1) # accept (-1, 1)
  
  x_out = tf.divide(x_atanh_2, 1.55) # atanhh(1.0/1.1)
  y = tf.identity(x_out, name='dnn_out') #please only preserve the x_out you want to test
  sess=tf.Session()
  
  sess.run(tf.global_variables_initializer())
  graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
  
  tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False)
  print("image_process.pb generated, please use \
  
  path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")
  output = sess.run(y, feed_dict=x : in_data)
  
  imageio.imsave("out.jpg", np.squeeze(output))
  Signed-off-by : Ting Fu <ting.fu@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.c
  • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.h
  • [DH] tools/python/convert_from_tensorflow.py
  • [DH] tools/python/convert_header.py