text: """
  =================================
  Gamma and log contrast adjustment
  =================================
  
  This example adjusts image contrast by performing a Gamma and a Logarithmic
  correction on the input image.
  
  """
  import matplotlib
  import matplotlib.pyplot as plt
  import numpy as np
  
  from skimage import data, img_as_float
  from skimage import exposure
  
  matplotlib.rcParams['font.size'] = 8
  
  
  def plot_img_and_hist(img, axes, bins=256):
      """Plot an image along with its histogram and cumulative histogram.
  
      """
      img = img_as_float(img)
      ax_img, ax_hist = axes
      ax_cdf = ax_hist.twinx()
  
      # Display image
      ax_img.imshow(img, cmap=plt.cm.gray)
      ax_img.set_axis_off()
  
      # Display histogram
      ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black')
      ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
      ax_hist.set_xlabel('Pixel intensity')
      ax_hist.set_xlim(0, 1)
      ax_hist.set_yticks([])
  
      # Display cumulative distribution
      img_cdf, bins = exposure.cumulative_distribution(img, bins)
      ax_cdf.plot(bins, img_cdf, 'r')
      ax_cdf.set_yticks([])
  
      return ax_img, ax_hist, ax_cdf
  
  
  # Load an example image
  img = data.moon()
  
  # Gamma
  gamma_corrected = exposure.adjust_gamma(img, 2)
  
  # Logarithmic
  logarithmic_corrected = exposure.adjust_log(img, 1)
  
  # Display results
  fig = plt.figure(figsize=(8, 5))
  axes = np.zeros((2, 3), dtype=np.object)
  axes[0, 0] = plt.subplot(2, 3, 1, adjustable='box-forced')
  axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0],
                           adjustable='box-forced')
  axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0],
                           adjustable='box-forced')
  axes[1, 0] = plt.subplot(2, 3, 4)
  axes[1, 1] = plt.subplot(2, 3, 5)
  axes[1, 2] = plt.subplot(2, 3, 6)
  
  ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
  ax_img.set_title('Low contrast image')
  
  y_min, y_max = ax_hist.get_ylim()
  ax_hist.set_ylabel('Number of pixels')
  ax_hist.set_yticks(np.linspace(0, y_max, 5))
  
  ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected, axes[:, 1])
  ax_img.set_title('Gamma correction')
  
  ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected, axes[:, 2])
  ax_img.set_title('Logarithmic correction')
  
  ax_cdf.set_ylabel('Fraction of total intensity')
  ax_cdf.set_yticks(np.linspace(0, 1, 5))
  
  # prevent overlap of y-axis labels
  fig.tight_layout()
  plt.show()
  