mirror of
https://github.com/wassname/scikit-image.git
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Tony S Yu
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
"""
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=================
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Template Matching
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=================
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In this example, we use template matching to identify the occurrence of an
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object in an image. The ``match_template`` function uses normalised correlation
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techniques to find instances of the "target image" in the "test image".
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The output of ``match_template`` is an image where we can easily identify peaks
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by eye. We mark the locations of matches (red dots), which are detected using
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a simple peak extraction algorithm. Note that the peaks in the output of
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``match_template`` correspond to the origin (i.e. top-left corner) of the
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template.
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"""
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import numpy as np
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from skimage.feature import match_template, peak_local_max
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from numpy.random import randn
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import matplotlib.pyplot as plt
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# We first construct a simple image target:
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size = 100
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target = np.tri(size) + np.tri(size)[::-1]
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# place target in an image at two positions, and add noise.
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image = np.zeros((400, 400))
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target_positions = [(50, 50), (200, 200)]
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for x, y in target_positions:
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image[x:x+size, y:y+size] = target
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image += randn(400, 400)*2
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# Match the template.
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result = match_template(image, target, method='norm-corr')
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found_positions = peak_local_max(result)
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if len(found_positions) > 2:
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# Keep the two maximum peaks.
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intensities = result[tuple(found_positions.T)]
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i_maxsort = np.argsort(intensities)[::-1]
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found_positions = found_positions[i_maxsort][:2]
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x_found, y_found = np.transpose(found_positions)
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fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(8, 3))
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plt.gray()
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ax0.imshow(target)
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ax0.set_title("Target image")
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ax1.imshow(image)
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ax1.plot(x_found, y_found, 'ro', alpha=0.5)
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ax1.set_title("Test image")
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ax1.autoscale(tight=True)
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ax2.imshow(result)
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ax2.plot(x_found, y_found, 'ro', alpha=0.5)
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ax2.set_title("Result from\n``match_template``")
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ax2.autoscale(tight=True)
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for ax in (ax0, ax1, ax2):
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ax.axis('off')
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plt.show()
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