text: #!/usr/bin/env python
  
  """Implementation and improvement of the paper:
  Unsupervised learning and segmentation of complex activities from video.
  """
  
  __author__ = 'Anna Kukleva'
  __date__ = 'June 2019'
  
  import sys
  import os
  sys.path.append(os.path.abspath('.').split('data_utils')[0])
  
  from ute.utils.arg_pars import opt
  from data_utils.YTI_utils.update_argpars import update
  from ute.ute_pipeline import temp_embed, all_actions
  
  
  if __name__ == '__main__':
  
      # set root
      opt.dataset_root = '/sequoia/data1/akukleva/projects/unsup_temp_embed/yti'
  
      # set activity
      # 'changing_tire', 'coffee', 'jump_car', 'cpr', 'repot'
      # all
      opt.subaction = 'coffee'
      # set feature extension and dimensionality
      opt.ext = 'txt'
      opt.feature_dim = 3000
  
      # model name can be 'mlp' or 'nothing' for no embedding (just raw features)
      opt.model_name = 'mlp'
  
      # do not load model
      opt.load_model = False
  
      # use background noise (e.g. for YTI)
      opt.bg = True
      opt.bg_trh = 75
  
      # update log name and absolute paths
      update()
  
      # run temporal embedding
      if opt.subaction == 'all':
          actions = ['changing_tire', 'coffee', 'jump_car', 'cpr', 'repot']
          all_actions(actions)
      else:
          temp_embed()
  
  
  