Python Image Processing Cookbook

How to do it...

To execute this recipe, perform the following steps:

1. Define the plot_image() and plot_hist() functions to display an image and return its cumulative distribution function (cdf), respectively:

def plot_image(image, title): plt.imshow(image) plt.title(title, size=20) plt.axis('off') def plot_hist(img): colors = ['r', 'g', 'b'] cdf = np.zeros((256,3)) for i in range(3):  hist, bins = np.histogram(img[...,i].flatten(),256,[0,256], \                normed=True)  cdf[...,i] = hist.cumsum()  cdf_normalized = cdf[...,i] * hist.max() / cdf.max()  plt.plot(cdf_normalized, color = colors[i], \                label='cdf ({})'.format(colors[i]))  binWidth = bins[1] - bins[0]  plt.bar(bins[:-1], hist*binWidth, binWidth,                 label='hist ({})'.format(colors[i])) plt.xlim([0,256]) ...