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https://github.com/wassname/scikit-image.git
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odebeir
72 lines
2.1 KiB
Python
72 lines
2.1 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 possible outcomes
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for a message.
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For an image, local entropy is related to the complexity contained in a given neighborhood, typically defined by a
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structuring element. A large number of various gray levels has a higher entropy than an homogeneous neighborhood.
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Entropy filter can detect subtle variations of local gray level distribution, in the example, the
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image is composed of two surfaces with two slightly different distributions.
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Image center has a random distribution in the range [-14,+14] centered on 128, while the borders has a
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random distribution in the range [-15,+15] centered on 128.
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We apply the local entropy measure using a circular structuring element of radius 10. As a result, one can
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detect the central square. Radius should be 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*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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plt.figure(figsize=[15, 5])
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plt.subplot(1, 3, 1)
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plt.imshow(noise_mask, cmap=plt.cm.gray)
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plt.xlabel('noise mask')
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plt.colorbar()
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plt.subplot(1, 3, 2)
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plt.imshow(img, cmap=plt.cm.gray)
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plt.xlabel('noised image')
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plt.colorbar()
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plt.subplot(1, 3, 3)
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plt.imshow(e)
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plt.xlabel('image local entropy ($r=%d$)' % radius)
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plt.colorbar()
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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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