parameters: (roidb)
return_statement: return data, label, im_info
docstring: return a dict of testbatch
      :param roidb: ['image', 'flipped'] + ['boxes']
      :return: data, label, im_info
docstring_summary: return a dict of testbatch
      :param roidb: ['image', 'flipped'] + ['boxes']
      :return: data, label, im_info
docstring_tokens: return a dict of testbatch : param roidb : [ image flipped ] + [ boxes ] : return : data label im_info
function: def get_rcnn_testbatch(roidb):
      """
      return a dict of testbatch
      :param roidb: ['image', 'flipped'] + ['boxes']
      :return: data, label, im_info
      """
      assert len(roidb) == 1, 'Single batch only'
      imgs, roidb = get_image(roidb)
      im_array = imgs[0]
      im_info = np.array([roidb[0]['im_info']], dtype=np.float32)
  
      im_rois = roidb[0]['boxes']
      rois = im_rois
      batch_index = 0 * np.ones((rois.shape[0], 1))
      rois_array = np.hstack((batch_index, rois))[np.newaxis, :]
  
      data = {'data': im_array,
              'rois': rois_array}
      label = {}
  
      return data, label, im_info
function_tokens: def get_rcnn_testbatch ( roidb ) : assert len ( roidb ) == 1 , 'Single batch only' imgs , roidb = get_image ( roidb ) im_array = imgs [ 0 ] im_info = np . array ( [ roidb [ 0 ] [ 'im_info' ] ] , dtype = np . float32 ) im_rois = roidb [ 0 ] [ 'boxes' ] rois = im_rois batch_index = 0 * np . ones ( ( rois . shape [ 0 ] , 1 ) ) rois_array = np . hstack ( ( batch_index , rois ) ) [ np . newaxis , : ] data = { 'data' : im_array , 'rois' : rois_array } label = { } return data , label , im_info