class_docstr: ObjectDetection dataset as DataFrame.
  
  Parameters
  ----------
  data : the input for pd.DataFrame
      The input data.
  dataset_type : str, optional
      The dataset type, can be voc/coco or more, used to annotate the optional fields.
  classes : list of str, optional
      The class synsets for this dataset, if `None`, it will infer from the data.
class_docstr_tok: ["ObjectDetection","dataset","as","DataFrame",".","Parameters","--","--","--","--","--","data",":","the","input","for","pd.DataFrame","The","input","data",".","dataset_type",":","str",",","optional","The","dataset","type",",","can","be","voc\/coco","or","more",",","used","to","annotate","the","optional","fields",".","classes",":","list","of","str",",","optional","The","class","synsets","for","this","dataset",",","if","`","None","`",",","it","will","infer","from","the","data","."]
docstr: Display images in dataset.
  
  Parameters
  ----------
  indices : iterable of int, optional
      The image indices to be displayed, if `None`, will generate `nsample` indices.
      If `shuffle` == `True`(default), the indices are random numbers.
  nsample : int, optional
      The number of samples to be displayed.
  ncol : int, optional
      The column size of ploted image matrix.
  shuffle : bool, optional
      If `shuffle` is False, will always sample from the begining.
  resize : int, optional
      The image will be resized to (resize, resize) for better visual experience.
  fontsize : int, optional, default is 20
      The fontsize for title
code: def show_images(
      self,
      indices=None,
      nsample=16,
      ncol=4,
      shuffle=True,
      resize=512,
      fontsize=20,
  ):
      r"""Display images in dataset.
  
      Parameters
      ----------
      indices : iterable of int, optional
          The image indices to be displayed, if `None`, will generate `nsample` indices.
          If `shuffle` == `True`(default), the indices are random numbers.
      nsample : int, optional
          The number of samples to be displayed.
      ncol : int, optional
          The column size of ploted image matrix.
      shuffle : bool, optional
          If `shuffle` is False, will always sample from the begining.
      resize : int, optional
          The image will be resized to (resize, resize) for better visual experience.
      fontsize : int, optional, default is 20
          The fontsize for title
      """
      df = self.pack()
      df = df.reset_index(drop=True)
      if indices is None:
          if not shuffle:
              indices = range(nsample)
          else:
              indices = list(range(len(df)))
              np.random.shuffle(indices)
              indices = indices[: min(nsample, len(indices))]
      images = [
          cv2.cvtColor(
              cv2.resize(
                  cv2.imread(df.at[idx, "image"]),
                  (resize, resize),
                  interpolation=cv2.INTER_AREA,
              ),
              cv2.COLOR_BGR2RGB,
          )
          for idx in indices
          if idx < len(df)
      ]
      # draw bounding boxes
      assert "rois" in df.columns
      for i, image in enumerate(images):
          height, width, _ = image.shape
          for roi in df.at[indices[i], "rois"]:
              xmin, ymin, xmax, ymax, cls_name = (
                  roi["xmin"],
                  roi["ymin"],
                  roi["xmax"],
                  roi["ymax"],
                  roi["class"],
              )
              xmin, xmax = (np.array([xmin, xmax]) * width).astype(
                  "int"
              )
              ymin, ymax = (np.array([ymin, ymax]) * height).astype(
                  "int"
              )
              if cls_name not in df.color_map:
                  df.color_map[cls_name] = tuple(
                      [np.random.randint(0, 255) for _ in range(3)]
                  )
              bcolor = df.color_map[cls_name]
              cv2.rectangle(
                  image,
                  (xmin, ymin),
                  (xmax, ymax),
                  bcolor,
                  2,
                  lineType=cv2.LINE_AA,
              )
          images[i] = image
  
      # reuse the color map
      if not self.color_map:
          self.color_map = df.color_map
  
      titles = [
          "Image(" + str(idx) + ")" for idx in indices if idx < len(df)
      ]
      _show_images(images, cols=ncol, titles=titles, fontsize=fontsize)
  
imports: import logging
  import os
  from pathlib import Path
  import warnings
  import numpy
  import pandas
  import matplotlib.pyplot
  from PIL import Image
  import cv2
  from ..data import is_url, url_data
  from ...utils.filesystem import import_try_install
  from ...utils.bbox import bbox_xywh_to_xyxy, bbox_clip_xyxy
label_desc: Use image node_id 12 for calling the ObjectDetectionDataset obj's underlying member method code with example usage: obj.show_images(indices, nsample, ncol, shuffle, resize, fontsize) without return types