mirror of
https://github.com/wassname/scikit-image.git
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martin
104 lines
3.0 KiB
Python
104 lines
3.0 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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ax_img.set_adjustable('box-forced')
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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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fig = plt.figure(figsize=(8, 5))
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axes = np.zeros((2,4), dtype=np.object)
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axes[0,0] = fig.add_subplot(2, 4, 1)
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for i in range(1,4):
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axes[0,i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])
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for i in range(0,4):
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axes[1,i] = fig.add_subplot(2, 4, 5+i)
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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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fig.subplots_adjust(wspace=0.4)
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plt.show()
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