61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
"""
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=================
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Template Matching
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=================
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We use template matching to identify the occurrence of an image patch
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(in this case, a sub-image centered on a single coin). Here, we
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return a single match (the exact same coin), so the maximum value in the
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``match_template`` result corresponds to the coin location. The other coins
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look similar, and thus have local maxima; if you expect multiple matches, you
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should use a proper peak-finding function.
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The ``match_template`` function uses fast, normalized cross-correlation [1]_
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to find instances of the template in the image. Note that the peaks in the
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output of ``match_template`` correspond to the origin (i.e. top-left corner) of
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the template.
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.. [1] J. P. Lewis, "Fast Normalized Cross-Correlation", Industrial Light and
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Magic.
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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 skimage import data
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from skimage.feature import match_template
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image = data.coins()
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coin = image[170:220, 75:130]
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result = match_template(image, coin)
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ij = np.unravel_index(np.argmax(result), result.shape)
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x, y = ij[::-1]
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fig = plt.figure(figsize=(8, 3))
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ax1 = plt.subplot(1, 3, 1)
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ax2 = plt.subplot(1, 3, 2)
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ax3 = plt.subplot(1, 3, 3, sharex=ax2, sharey=ax2)
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ax1.imshow(coin, cmap=plt.cm.gray)
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ax1.set_axis_off()
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ax1.set_title('template')
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ax2.imshow(image, cmap=plt.cm.gray)
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ax2.set_axis_off()
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ax2.set_title('image')
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# highlight matched region
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hcoin, wcoin = coin.shape
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rect = plt.Rectangle((x, y), wcoin, hcoin, edgecolor='r', facecolor='none')
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ax2.add_patch(rect)
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ax3.imshow(result)
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ax3.set_axis_off()
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ax3.set_title('`match_template`\nresult')
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# highlight matched region
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ax3.autoscale(False)
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ax3.plot(x, y, 'o', markeredgecolor='r', markerfacecolor='none', markersize=10)
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plt.show()
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