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• Minimum FFMPEG configure options for H264 decoding

  26 novembre 2019, par Big Guy

  I’m working on a project using an LS1021A board, which has a Cortex-A7, NEON, FPU, etc, which seems to be very similar to a Raspberry Pi 2. We are streaming in H264 via RTP (UDP) with VLC to simulate an IP Camera with the end goal of extracting the raw RGB frame data (in a C app) to be used elsewhere in the system.

  So, I’d like to compile ffmpeg and the associated libs to do only H264 decoding, using libavcodec in C code. This will be running on a custom linux OS with tight size constraints, so lib size needs to be as small as possible.

  I have the following so far after a day or so of googling :

  ./configure --disable-encoders --disable-decoders --enable-decoder=h264 --enable-small --enable-cross-compile --arch-arm --target-os=linux --cpu=cortex-a7 --disable-debug --cross-prefix=${GCC_PREFIX} --prefix=/c/Users/labuser/Desktop/build --extra-cflags='march=armv7-a -mtune=cortex-a7 -mfpu=neon-vfpv4 -mfloat-abi=hard' --enable-neon --disable-devices --disable-sdl --disable-ffprobe --disable-ffserver --disable-doc --disable-w32threads --disable-ffplay

  Since I’ll be using libavcodec directly, I’m not sure if some of these —disable switches are needed, but can anyone give any pointers on if this looks acceptable or if there are any other switches I should be using ?

  Thanks !

• configure : Increase minimum libx265 version

  8 janvier 2020, par Andriy Gelman

  configure : Increase minimum libx265 version
  libx265.c references a member x265_picture.quantOffsets (for ROI
  
  support) which was added in X265_BUILD 70. Increase the minimum libx265
  
  version to fix compilation.
  Signed-off-by : Andriy Gelman <andriy.gelman@gmail.com>
  
  Reviewed-by : Derek Buitenhuis <derek.buitenhuis@gmail.com>
  
  Signed-off-by : Michael Niedermayer <michael@niedermayer.cc>
  

  • [DH] configure

• dnn/native : add native support for minimum

  26 avril 2020, par Guo, Yejun

  dnn/native : add native support for minimum
  it can be tested with model file generated with below python script :
  
  import tensorflow as tf
  
  import numpy as np
  
  import imageio
  in_img = imageio.imread('input.jpg')
  
  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.minimum(0.7, x)
  
  x2 = tf.maximum(x1, 0.4)
  
  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 : Guo, Yejun <yejun.guo@intel.com>
  

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