class_docstr: Superclass for monocular dataloaders
  Parameters
  ----------
  data_path : string
      Path to dataset folder.
  filenames : string
      Path to split file.
      For example: '$(HOME)/.mxnet/datasets/kitti/splits/eigen_full/train_files.txt'
  height : int
      The height for input images.
  width : int
      The height for input images.
  frame_idxs : list
      The frames to load.
      an integer (e.g. 0, -1, or 1) representing the temporal step relative to 'index',
      or "s" for the opposite image in the stereo pair.
  num_scales : int
      The number of scales of the image relative to the full-size image.
  is_train : bool
      Whether use Data Augmentation. Default is: False
  img_ext : string
      The extension name of input image. Default is '.jpg'
class_docstr_tok: ["Superclass","for","monocular","dataloaders","Parameters","--","--","--","--","--","data_path",":","string","Path","to","dataset","folder",".","filenames",":","string","Path","to","split","file",".","For","example",":","'","$","(","HOME",")","\/.mxnet\/datasets\/kitti\/splits\/eigen_full\/train_files.txt'","height",":","int","The","height","for","input","images",".","width",":","int","The","height","for","input","images",".","frame_idxs",":","list","The","frames","to","load",".","an","integer","(","e.g",".","0",",","-1",",","or","1",")","representing","the","temporal","step","relative","to","'index","'",",","or","``","s","''","for","the","opposite","image","in","the","stereo","pair",".","num_scales",":","int","The","number","of","scales","of","the","image","relative","to","the","full-size","image",".","is_train",":","bool","Whether","use","Data","Augmentation",".","Default","is",":","False","img_ext",":","string","The","extension","name","of","input","image",".","Default","is","'.jpg","'"]
code: def __init__(
      self,
      data_path,
      filenames,
      height,
      width,
      frame_idxs,
      num_scales,
      is_train=False,
      img_ext=".jpg",
  ):
      super(MonoDataset, self).__init__()
  
      self.data_path = data_path
      self.filenames = filenames
      self.height = height
      self.width = width
      self.num_scales = num_scales
      self.interp = Image.ANTIALIAS
  
      self.frame_idxs = frame_idxs
  
      self.is_train = is_train
      self.img_ext = img_ext
  
      self.loader = pil_loader
      self.to_tensor = transforms.ToTensor()
  
      self.brightness = 0.2
      self.contrast = 0.2
      self.saturation = 0.2
      self.hue = 0.1
  
      self.load_depth = self.check_depth()
  
file_path: gluon-cv/gluoncv/data/kitti/mono_dataset.py
imports: import random
  import copy
  import numpy
  from PIL import Image
  import mxnet
  from mxnet.gluon.data import dataset
  from mxnet.gluon.data.vision import transforms
label_desc: Use image node_id 1 to create a new MonoDataset object from inherited base classes: dataset with example: obj = MonoDataset(data_path, filenames, height, width, frame_idxs, num_scales, is_train, img_ext)