78 lines
2.3 KiB
Python
78 lines
2.3 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. The entropy filter can
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detect subtle variations in the local gray level distribution.
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In the first example, the image is composed of two surfaces with two slightly
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different distributions. The image has a uniform random distribution in the
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range [-15, +15] in the middle of the image and a uniform random distribution in
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the range [-14, 14] at the image borders, both centered at a gray value of 128.
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To detect the central square, we compute the local entropy measure using a
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circular structuring element of a radius big enough to capture the local gray
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level distribution. The second example shows how to detect texture in the camera
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image using a smaller structuring element.
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"""
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######################################################################
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# Object detection
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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 = np.full((128, 128), 28, 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) - 0.5 *
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noise_mask).astype(np.uint8)
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img = noise + 128
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entr_img = entropy(img, disk(10))
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fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(10, 4))
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img0 = ax0.imshow(noise_mask, cmap='gray')
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ax0.set_title("Object")
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ax1.imshow(img, cmap='gray')
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ax1.set_title("Noisy image")
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ax2.imshow(entr_img, cmap='viridis')
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ax2.set_title("Local entropy")
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fig.tight_layout()
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######################################################################
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# Texture detection
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# =================
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image = img_as_ubyte(data.camera())
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fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(12, 4),
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sharex=True, sharey=True)
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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='gray')
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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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fig.tight_layout()
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plt.show()
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