This example demonstrates the feature of histogram matching. It manipulates thepixels of an input image so that its histogram matches the histogram of thereference image. If the images have multiple channels, the matching is doneindependently for each channel, as long as the number of channels is equal inthe input image and the reference.
Histogram matching can be used as a lightweight normalisation for imageprocessing, such as feature matching, especially in circumstances where theimages have been taken from different sources or in different conditions (i.e.lighting).
import matplotlib.pyplot as pltfrom skimage import datafrom skimage import exposurefrom skimage.transform import match_histogramsreference = data.coffee()image = data.chelsea()matched = match_histograms(image, reference, multichannel=True)fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3), sharex=True, sharey=True)for aa in (ax1, ax2, ax3): aa.set_axis_off()ax1.imshow(image)ax1.set_title('Source')ax2.imshow(reference)ax2.set_title('Reference')ax3.imshow(matched)ax3.set_title('Matched')plt.tight_layout()plt.show()
To illustrate the effect of the histogram matching, we plot for eachRGB channel, the histogram and the cumulative histogram. Clearly,the matched image has the same cumulative histogram as the referenceimage for each channel.
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(8, 8))for i, img in enumerate((image, reference, matched)): for c, c_color in enumerate(('red', 'green', 'blue')): img_hist, bins = exposure.histogram(img[..., c], source_range='dtype') axes[c, i].plot(bins, img_hist / img_hist.max()) img_cdf, bins = exposure.cumulative_distribution(img[..., c]) axes[c, i].plot(bins, img_cdf) axes[c, 0].set_ylabel(c_color)axes[0, 0].set_title('Source')axes[0, 1].set_title('Reference')axes[0, 2].set_title('Matched')plt.tight_layout()plt.show()
Total running time of the script: ( 0 minutes 0.433 seconds)
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