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• FFmpeg proccess killed while converting .mov file

  10 novembre 2020, par Vala Khosravi

  I'm using FFmpeg to reduce my videos file size when I give a .mov file as an input with this command :

  


  ffmpeg -i in.mov -c:a copy -crf 20 out.mov


  


  program start working and after a while, it gets killed. here are the last lines of the log that I get :

  


  Output #0, mov, to '/home/ubuntu/test.mov':
  Metadata:
    major_brand     : qt
    minor_version   : 0
    compatible_brands: qt
    com.apple.quicktime.creationdate: 2020-08-29T15:03:17+0430
    com.apple.quicktime.make: Apple
    com.apple.quicktime.model: MacBookPro14,1
    com.apple.quicktime.software: Mac OS X 10.15.1 (19B88)
    encoder         : Lavf57.83.100
    Stream #0:0(und): Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 2866x1716 [SAR 1:1 DAR 1433:858], q=-1--1, 60 fps, 15360 tbn, 60 tbc (default)
    Metadata:
      creation_time   : 2020-11-10T09:04:43.000000Z
      handler_name    : Core Media Data Handler
      encoder         : Lavc57.107.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 207 kb/s (default)
    Metadata:
      creation_time   : 2020-11-10T09:04:43.000000Z
      handler_name    : Core Media Data Handler
Killed   23 fps=3.6 q=0.0 size=       0kB time=00:00:01.34 bitrate=   0.0kbits/s dup=1 drop=0 speed=0.21x


  


  I tried so many different flags for FFmpeg but still getting the same error.

  


  What's the solution ?
here is my input video file

  


• dnn/openvino : support run inference via GPU

  9 septembre 2020, par Ting Fu

  dnn/openvino : support run inference via GPU
  for enabling OpenVINO GPU please :
  
  1. install required OpenCL drivers, see : https://github.com/intel/compute-runtime/releases/tag/19.41.14441
  
  2. build OpenVINO c lib with GPU enabled : use cmake config with : -DENABLE_CLDNN=ON
  
  3. then make, and include the OpenVINO c lib in environment variables
  
  detailed steps please refer : https://github.com/openvinotoolkit/openvino/blob/master/build-instruction.md
  inference model with GPU please add : optioins=device=GPU
  Signed-off-by : Ting Fu <ting.fu@intel.com>
  
  Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_openvino.c

• dnn_backend_native_layer_mathunary : add sin support

  6 juin 2020, par Ting Fu

  dnn_backend_native_layer_mathunary : add sin support
  It can be tested with the model file generated with below python scripy :
  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.multiply(x, 3.14)
  
  x2 = tf.sin(x1)
  
  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