mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 01:43:45 +08:00
Olivier Debeir
73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
"""
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=======
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Entropy
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=======
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In information theory, information entropy is the log-base-2 of the number of
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possible outcomes for a message.
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For an image, local entropy is related to the complexity contained in a given
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neighborhood, typically defined by a structuring element.
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The entropy filter can detect subtle variations in the local gray level
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distribution.
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In the example, the image is composed of two surfaces with two slightly
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different distributions.
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The image has a uniform random distribution in the range [-14, +14] in the
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middle of the image and a uniform random distribution in the range [-15, 15]
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at the image borders, both centered at a gray value of 128.
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We apply the local entropy measure using a circular structuring element of
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radius 10. As a result, one can detect the central square. The radius is
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big enough to efficiently sample the local gray level distribution.
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In the second example, the local entropy is used to detect image texture.
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"""
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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
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from skimage.util import img_as_ubyte
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from skimage.filters.rank import entropy
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from skimage.morphology import disk
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noise_mask = 28 * np.ones((128, 128), dtype=np.uint8)
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noise_mask[32:-32, 32:-32] = 30
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noise = (noise_mask * np.random.random(noise_mask.shape) - .5 *
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noise_mask).astype(np.uint8)
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img = noise + 128
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radius = 10
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e = entropy(img, disk(radius))
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fig, ax = plt.subplots(1, 3, figsize=(8, 5))
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ax1, ax2, ax3 = ax.ravel()
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ax1.imshow(noise_mask, cmap=plt.cm.gray)
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ax1.set_xlabel('Noise mask')
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ax2.imshow(img, cmap=plt.cm.gray)
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ax2.set_xlabel('Noised image')
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ax3.imshow(e)
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ax3.set_xlabel('Local entropy ($r=%d$)' % radius)
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# second example: texture detection
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image = img_as_ubyte(data.camera())
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fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 4))
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img0 = ax0.imshow(image, cmap=plt.cm.gray)
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ax0.set_title('Image')
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ax0.axis('off')
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fig.colorbar(img0, ax=ax0)
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img1 = ax1.imshow(entropy(image, disk(5)), cmap=plt.cm.jet)
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ax1.set_title('Entropy')
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ax1.axis('off')
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fig.colorbar(img1, ax=ax1)
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plt.show()
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