mirror of
https://github.com/wassname/scikit-image.git
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Tony S Yu
75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
"""
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======================
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Histogram Equalization
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======================
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This examples takes an image with low contrast and enhances its contrast using
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histogram equalization. Histogram equalization enhances contrast by "spreading
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out the most frequent intensity values" in an image [1]_. The equalized image
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has a roughly linear cumulative distribution function, as shown in this example.
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For comparison, this example also shows an image after its intensity levels are
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uniformly stretched.
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.. [1] http://en.wikipedia.org/wiki/Histogram_equalization
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"""
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import matplotlib.pyplot as plt
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from matplotlib import ticker
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from skimage import data
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from skimage.util.dtype import dtype_range
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from skimage import exposure
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def plot_hist(img, bins=256):
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"""Plot histogram and cumulative histogram for image"""
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img_cdf, bins = exposure.cumulative_distribution(img, bins)
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plt.hist(img.ravel(), bins=bins)
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plt.xlabel('Pixel intensiy')
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# Shorten y-tick labels using scientific notation
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y_formatter = ticker.ScalarFormatter(useOffset=True)
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y_formatter.set_powerlimits((0, 0)) # force use of scientific notation
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ax = plt.gca()
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ax.yaxis.set_major_formatter(y_formatter)
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ax_cdf = plt.twinx()
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ax_cdf.plot(bins, img_cdf, 'r')
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xmin, xmax = dtype_range[img.dtype.type]
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plt.xlim(xmin, xmax)
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img_orig = data.camera()
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# squeeze image intensities to lower image contrast
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img = img_orig / 5 + 100
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img_rescale = exposure.rescale_intensity(img)
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img_eq = exposure.equalize(img)
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plt.subplot(2, 3, 1)
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plt.imshow(img, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.title('Low contrast image')
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plt.axis('off')
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plt.subplot(2, 3, 4)
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plt.ylabel('Number of pixels')
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plot_hist(img)
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plt.subplot(2, 3, 2)
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plt.imshow(img_rescale, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.title('Rescale intensities')
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plt.axis('off')
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plt.subplot(2, 3, 5)
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plot_hist(img_rescale)
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plt.subplot(2, 3, 3)
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plt.imshow(img_eq, cmap=plt.cm.gray, vmin=0, vmax=1)
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plt.title('Histogram equalization')
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plt.axis('off')
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plt.subplot(2, 3, 6)
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plot_hist(img_eq)
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plt.ylabel('Fraction of total intensity')
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# prevent overlap of y-axis labels
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plt.subplots_adjust(wspace=0.4)
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plt.show()
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