class_docstr: Reads data suitable for object detection.
  
  The object detection task needs the following information:
      - images.
      - ground truth, rectangular bounding boxes with associated labels.
  
  For implementing a subclass of object detection, one needs to implement
  the following methods:
      - __init__
      - get_total
      - get_classes
      - iterate
  
  Optionally:
      - pretty_name
  
  Additionally, must use the `_per_class_counter` variable to honor the max
  number of examples per class in an efficient way.
class_docstr_tok: ["Reads","data","suitable","for","object","detection",".","The","object","detection","task","needs","the","following","information",":","-","images",".","-","ground","truth",",","rectangular","bounding","boxes","with","associated","labels",".","For","implementing","a","subclass","of","object","detection",",","one","needs","to","implement","the","following","methods",":","-","__init__","-","get_total","-","get_classes","-","iterate","Optionally",":","-","pretty_name","Additionally",",","must","use","the","`","_per_class_counter","`","variable","to","honor","the","max","number","of","examples","per","class","in","an","efficient","way","."]
docstr: Returns the total number of records in the dataset.
docstr_tok: ["Returns","the","total","number","of","records","in","the","dataset","."]
code: def get_total(self):
      """
      Returns the total number of records in the dataset.
      """
  
imports: import abc
  import six
  import tensorflow
  from collections import Counter
  from luminoth.tools.dataset.readers import BaseReader
label_desc: Use image node_id 5 for calling the ObjectDetectionReader obj's underlying member method code with example usage: obj.get_total() without return types