text: """
      Borrowed from https://github.com/delestro/rand_cmap.
  """
  
  # Generate random colormap
  def rand_cmap(nlabels, type='bright', first_color_black=True, last_color_black=False, verbose=True):
      """
      Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks
      :param nlabels: Number of labels (size of colormap)
      :param type: 'bright' for strong colors, 'soft' for pastel colors
      :param first_color_black: Option to use first color as black, True or False
      :param last_color_black: Option to use last color as black, True or False
      :param verbose: Prints the number of labels and shows the colormap. True or False
      :return: colormap for matplotlib
      """
      from matplotlib.colors import LinearSegmentedColormap
      import colorsys
      import numpy as np
  
      if type not in ('bright', 'soft'):
          print ('Please choose "bright" or "soft" for type')
          return
  
      if verbose:
          print('Number of labels: ' + str(nlabels))
  
      # Generate color map for bright colors, based on hsv
      if type == 'bright':
          randHSVcolors = [(np.random.uniform(low=0.0, high=1),
                            np.random.uniform(low=0.2, high=1),
                            np.random.uniform(low=0.9, high=1)) for i in range(nlabels)]
  
          # Convert HSV list to RGB
          randRGBcolors = []
          for HSVcolor in randHSVcolors:
              randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))
  
          if first_color_black:
              randRGBcolors[0] = [0, 0, 0]
  
          if last_color_black:
              randRGBcolors[-1] = [0, 0, 0]
  
          random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
  
      # Generate soft pastel colors, by limiting the RGB spectrum
      if type == 'soft':
          low = 0.6
          high = 0.95
          randRGBcolors = [(np.random.uniform(low=low, high=high),
                            np.random.uniform(low=low, high=high),
                            np.random.uniform(low=low, high=high)) for i in range(nlabels)]
  
          if first_color_black:
              randRGBcolors[0] = [0, 0, 0]
  
          if last_color_black:
              randRGBcolors[-1] = [0, 0, 0]
          random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
  
      # Display colorbar
      if verbose:
          from matplotlib import colors, colorbar
          from matplotlib import pyplot as plt
          fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))
  
          bounds = np.linspace(0, nlabels, nlabels + 1)
          norm = colors.BoundaryNorm(bounds, nlabels)
  
          cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None,
                                     boundaries=bounds, format='%1i', orientation=u'horizontal')
  
      return random_colormap
  
  