mirror of
https://github.com/wassname/scikit-image.git
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Johannes Schönberger
90 lines
2.3 KiB
Python
90 lines
2.3 KiB
Python
"""
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============================
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Local Histogram Equalization
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============================
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This examples enhances an image with low contrast, using a method called *local
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histogram equalization*, which spreads out the most frequent intensity values in
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an image.
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The equalized image [1]_ has a roughly linear cumulative distribution function
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for each pixel neighborhood.
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The local version [2]_ of the histogram equalization emphasized every local
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graylevel variations.
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References
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----------
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.. [1] http://en.wikipedia.org/wiki/Histogram_equalization
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.. [2] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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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.util import img_as_ubyte
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from skimage import exposure
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from skimage.morphology import disk
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from skimage.filter import rank
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def plot_img_and_hist(img, axes, bins=256):
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"""Plot an image along with its histogram and cumulative histogram.
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"""
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ax_img, ax_hist = axes
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ax_cdf = ax_hist.twinx()
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# Display image
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ax_img.imshow(img, cmap=plt.cm.gray)
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ax_img.set_axis_off()
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# Display histogram
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ax_hist.hist(img.ravel(), bins=bins)
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ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
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ax_hist.set_xlabel('Pixel intensity')
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xmin, xmax = dtype_range[img.dtype.type]
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ax_hist.set_xlim(xmin, xmax)
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# Display cumulative distribution
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img_cdf, bins = exposure.cumulative_distribution(img, bins)
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ax_cdf.plot(bins, img_cdf, 'r')
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return ax_img, ax_hist, ax_cdf
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# Load an example image
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img = img_as_ubyte(data.moon())
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# Contrast stretching
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p2 = np.percentile(img, 2)
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p98 = np.percentile(img, 98)
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img_rescale = exposure.equalize_hist(img)
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# Equalization
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selem = disk(30)
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img_eq = rank.equalize(img, selem=selem)
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# Display results
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f, axes = plt.subplots(2, 3, figsize=(8, 4))
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ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
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ax_img.set_title('Low contrast image')
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ax_hist.set_ylabel('Number of pixels')
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ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
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ax_img.set_title('Global equalise')
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ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
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ax_img.set_title('Local equalize')
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ax_cdf.set_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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