mirror of
https://github.com/wassname/scikit-image.git
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Tony S Yu
87c2353845
`equalize_hist`: "max_intensity" parameter no longer exists---img_as_float normalizes intensity range `cumulative_distribution`: Return centers of bins instead of the edges. Move `histogram` function from filter subpackage. Add test of equalize_hist Add example of histogram equalization.
56 lines
1.5 KiB
Python
56 lines
1.5 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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.. [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 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, ax=None):
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"""Plot histogram and cumulative histogram for image"""
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ax = ax if ax is not None else plt.gca()
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img_cdf, bins = exposure.cumulative_distribution(img, bins)
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ax.hist(img.ravel(), bins=bins)
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ax_right = ax.twinx()
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ax_right.plot(bins, img_cdf, 'r')
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xmin, xmax = dtype_range[img.dtype.type]
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ax.set_xlim(xmin, xmax)
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ax.set_ylabel('# pixels')
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ax.set_xlabel('pixel intensiy')
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ax_right.set_ylabel('fraction of total intensity')
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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_eq = exposure.equalize_hist(img)
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plt.subplot(2, 2, 1)
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plt.imshow(img, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.axis('off')
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plt.subplot(2, 2, 2)
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plot_hist(img)
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plt.subplot(2, 2, 3)
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plt.imshow(img_eq, cmap=plt.cm.gray, vmin=0, vmax=1)
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plt.axis('off')
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plt.subplot(2, 2, 4)
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plot_hist(img_eq)
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plt.subplots_adjust(left=0.05, hspace=0.25, wspace=0.3, top=0.95, bottom=0.1)
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plt.show()
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