mirror of
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Julian Taylor
d4cb154d17
percentile can compute multiple ranks in one go which in the case of the example halves the required time.
97 lines
2.7 KiB
Python
97 lines
2.7 KiB
Python
"""
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======================
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Histogram Equalization
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======================
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This examples enhances an image with low contrast, using a method called
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*histogram equalization*, which "spreads out the most frequent intensity
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values" in an image [1]_. The equalized image has a roughly linear cumulative
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distribution function.
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While histogram equalization has the advantage that it requires no parameters,
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it sometimes yields unnatural looking images. An alternative method is
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*contrast stretching*, where the image is rescaled to include all intensities
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that fall within the 2nd and 98th percentiles [2]_.
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.. [1] http://en.wikipedia.org/wiki/Histogram_equalization
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.. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm
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"""
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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from skimage import data, img_as_float
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from skimage import exposure
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matplotlib.rcParams['font.size'] = 8
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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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img = img_as_float(img)
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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, histtype='step', color='black')
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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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ax_hist.set_xlim(0, 1)
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ax_hist.set_yticks([])
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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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ax_cdf.set_yticks([])
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return ax_img, ax_hist, ax_cdf
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# Load an example image
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img = data.moon()
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# Contrast stretching
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p2, p98 = np.percentile(img, (2, 98))
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img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
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# Equalization
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img_eq = exposure.equalize_hist(img)
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# Adaptive Equalization
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img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
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# Display results
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f, axes = plt.subplots(nrows=2, ncols=4, figsize=(8, 5))
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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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y_min, y_max = ax_hist.get_ylim()
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ax_hist.set_ylabel('Number of pixels')
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ax_hist.set_yticks(np.linspace(0, y_max, 5))
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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('Contrast stretching')
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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('Histogram equalization')
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ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])
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ax_img.set_title('Adaptive equalization')
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ax_cdf.set_ylabel('Fraction of total intensity')
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ax_cdf.set_yticks(np.linspace(0, 1, 5))
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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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