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Ankit Agrawal
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
"""
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=========================
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CenSurE Feature Detection
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=========================
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In this example we detect and plot the CenSurE (Center Surround Extrema)
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features at various scales using Difference of Boxes, Octagon and Star shaped
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bi-level filters.
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"""
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from skimage.feature import keypoints_censure
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from skimage.data import lena
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from skimage.color import rgb2gray
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import matplotlib.pyplot as plt
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# Initializing the parameters for Censure keypoints
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img = lena()
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gray_img = rgb2gray(img)
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min_scale = 2
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max_scale = 6
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non_max_threshold = 0.15
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line_threshold = 10
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_, ax = plt.subplots(nrows=(max_scale - min_scale - 1), ncols=3,
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figsize=(6, 6))
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plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.94,
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bottom=0.02, left=0.06, right=0.98)
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# Detecting Censure keypoints for the following filters
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for col, mode in enumerate(['dob', 'octagon', 'star']):
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ax[0, col].set_title(mode.upper(), fontsize=12)
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keypoints, scales = keypoints_censure(gray_img, min_scale, max_scale,
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mode, non_max_threshold,
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line_threshold)
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# Plotting Censure features at all the scales
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for row, scale in enumerate(range(min_scale + 1, max_scale)):
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mask = scales == scale
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x = keypoints[mask, 1]
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y = keypoints[mask, 0]
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s = 0.5 * 2 ** (scale + min_scale + 1)
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ax[row, col].imshow(img)
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ax[row, col].scatter(x, y, s, facecolors='none', edgecolors='b')
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ax[row, col].set_xticks([])
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ax[row, col].set_yticks([])
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ax[row, col].axis((0, img.shape[1], img.shape[0], 0))
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if col == 0:
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ax[row, col].set_ylabel('Scale %d' % scale, fontsize=12)
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plt.show()
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