56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
"""
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===================
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Canny edge detector
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===================
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The Canny filter is a multi-stage edge detector. It uses a filter based on the
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derivative of a Gaussian in order to compute the intensity of the gradients.The
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Gaussian reduces the effect of noise present in the image. Then, potential
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edges are thinned down to 1-pixel curves by removing non-maximum pixels of the
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gradient magnitude. Finally, edge pixels are kept or removed using hysteresis
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thresholding on the gradient magnitude.
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The Canny has three adjustable parameters: the width of the Gaussian (the
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noisier the image, the greater the width), and the low and high threshold for
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the hysteresis thresholding.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy import ndimage as ndi
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from skimage import feature
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# Generate noisy image of a square
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im = np.zeros((128, 128))
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im[32:-32, 32:-32] = 1
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im = ndi.rotate(im, 15, mode='constant')
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im = ndi.gaussian_filter(im, 4)
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im += 0.2 * np.random.random(im.shape)
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# Compute the Canny filter for two values of sigma
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edges1 = feature.canny(im)
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edges2 = feature.canny(im, sigma=3)
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# display results
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fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),
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sharex=True, sharey=True)
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ax1.imshow(im, cmap=plt.cm.gray)
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ax1.axis('off')
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ax1.set_title('noisy image', fontsize=20)
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ax2.imshow(edges1, cmap=plt.cm.gray)
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ax2.axis('off')
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ax2.set_title(r'Canny filter, $\sigma=1$', fontsize=20)
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ax3.imshow(edges2, cmap=plt.cm.gray)
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ax3.axis('off')
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ax3.set_title(r'Canny filter, $\sigma=3$', fontsize=20)
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fig.tight_layout()
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plt.show()
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