old_contents: import numpy as np
  
  
  def vis_bbox(img, bbox, label_names=None, ax=None):
      """Visualize bounding boxes inside image
  
      For notation, `H` and `W` are height and width of a image respectively.
      `R` is the number of bounding boxes in a image.
  
      Example:
  
          >>> import chainercv
          >>> import matplotlib.pyplot as plot
          >>> dataset = chainercv.datasets.VOCDetectionDataset()
          >>> img, bbox = dataset[10]
          >>> img = chainercv.transforms.chw_to_pil_image(img)
          >>> chainercv.tasks.vis_bbox(img, bbox, dataset.labels)
          >>> plot.show()
  
  
      Args:
          img (numpy.ndarray): array of shape (H, W, 3). This is in RGB format.
          bbox (numpy.ndarray): array of shape (R, 5). Elements are organized
              by (x_min, y_min, x_max, y_max, label_id).
          label_names (iterable of strings): name of labels ordered according
              to label_ids. If this is `None`, labels will be skipped.
          ax (matplotlib.axes.Axis): The visualization is displayed on this
              axis. If this is None, new axis is created.
  
      """
      from matplotlib import pyplot as plot
      if ax is None:
          fig = plot.figure()
          ax = fig.add_subplot(1, 1, 1)
      ax.imshow(img)
  
      for bbox_elem in bbox:
          xy = (bbox_elem[0], bbox_elem[1])
          width = bbox_elem[2] - bbox_elem[0]
          height = bbox_elem[3] - bbox_elem[1]
          ax.add_patch(plot.Rectangle(
              xy, width, height, fill=False, edgecolor='red', linewidth=3))
          if label_names is not None:
              ax.text(bbox_elem[0], bbox_elem[1],
                      label_names[bbox_elem[4].astype(np.int)],
                      style='italic',
                      bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 10})
  
new_contents: import numpy as np
  
  
  def vis_bbox(img, bbox, label_names=None, ax=None):
      """Visualize bounding boxes inside image.
  
      Example:
  
          >>> import chainercv
          >>> import matplotlib.pyplot as plot
          >>> dataset = chainercv.datasets.VOCDetectionDataset()
          >>> img, bbox = dataset[60]
          >>> img = chainercv.transforms.chw_to_pil_image(img)
          >>> chainercv.tasks.vis_bbox(img, bbox, dataset.labels)
          >>> plot.show()
  
  
      Args:
          img (numpy.ndarray): array of shape :math:`(height, width, 3)`.
              This is in RGB format.
          bbox (numpy.ndarray): array of shape :math:`(R, 5)`, where :math:`R` is
              the number of bounding boxes in the image.. Elements are organized
              by (x_min, y_min, x_max, y_max, label_id) in the second axis.
          label_names (iterable of strings): name of labels ordered according
              to label_ids. If this is :obj:`None`, labels will be skipped.
          ax (matplotlib.axes.Axis): The visualization is displayed on this
              axis. If this is :obj:`None` (default), new axis is created.
  
      """
      from matplotlib import pyplot as plot
      if ax is None:
          fig = plot.figure()
          ax = fig.add_subplot(1, 1, 1)
      ax.imshow(img)
  
      for bbox_elem in bbox:
          xy = (bbox_elem[0], bbox_elem[1])
          width = bbox_elem[2] - bbox_elem[0]
          height = bbox_elem[3] - bbox_elem[1]
          ax.add_patch(plot.Rectangle(
              xy, width, height, fill=False, edgecolor='red', linewidth=3))
          if label_names is not None:
              ax.text(bbox_elem[0], bbox_elem[1],
                      label_names[bbox_elem[4].astype(np.int)],
                      style='italic',
                      bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 10})
  
subject: fix doc of vis_bbox