mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 04:55:16 +08:00
Tony S Yu
70 lines
1.9 KiB
Python
70 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.
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"""
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import numpy as np
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from skimage.feature import match_template
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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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# peak extraction algorithm.
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delta = 5
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found_positions = []
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for i in range(50):
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index = np.argmax(result)
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y, x = np.unravel_index(index, result.shape)
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if not found_positions:
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found_positions.append((x, y))
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for position in found_positions:
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distance = np.sqrt((x - position[0]) ** 2 + (y - position[1]) ** 2)
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if distance > delta:
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found_positions.append((x, y))
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result[y, x] = 0
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if len(found_positions) == len(target_positions):
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break
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x_found, y_found = np.transpose(found_positions)
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plt.gray()
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plt.subplot(1, 3, 1)
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plt.imshow(target)
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plt.title("Target image")
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plt.axis('off')
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plt.subplot(1, 3, 2)
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plt.imshow(image)
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plt.title("Test image")
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plt.axis('off')
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plt.subplot(1, 3, 3)
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plt.imshow(result)
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plt.plot(x_found, y_found, 'ro')
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plt.title("Result from\n``match_template``")
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plt.autoscale(tight=True)
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plt.axis('off')
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plt.show()
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