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• Raspberry Pi cannot connect to IP camera, while Windows PC can ?

  28 novembre 2020, par gf000

  I have an IP camera (Provision ISR, DI-320IPS-VF, 2MP IR Vari-Focal Dome IP Camera). When I connect it to my router (it is a 10 or 12 years old TP-LINK TL-WR1043ND), it will immediately show up in "IP Manager" (this is a program by the manufacturer) on my Windows PC. If I double click on the camera's row, Internet Explorer opens up, and everything is working fine. On the same Windows PC, if I open VLC Media Player, and paste this URL : rtsp ://username:password@localipaddress:554/profile1
It works fine.

  


  I have also a "Raspberry Pi 4 model B 4 GB RAM", which is connected to the same router. If I open VLC Media Player, and I paste the same URL, it doesn't work. I also created an ffmpeg one-liner, which would take a photo, but that is also not working.

  


  My final goal would be to be able to take photos with that ffmpeg one-liner, on the Raspberry Pi.

  


  Maybe the Raspberry Pi is not powerful enough to handle the stream ? Or, could it be the problem that the IP-camera is sending H-264 / H-265 video stream, and the Raspberry Pi cannot handle it ?

  


  Thank you for your help. Any idea could help me.

  


• dnn_backend_native_layer_mathunary : add acos support

  18 juin 2020, par Ting Fu

  dnn_backend_native_layer_mathunary : add acos 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.acos(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

• dnn_backend_native_layer_mathunary : add atan support

  18 juin 2020, par Ting Fu

  dnn_backend_native_layer_mathunary : add atan 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.atan(x)
  
  x2 = tf.divide(x1, 3.1416/4) # pi/4
  
  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