mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 00:39:44 +08:00
Tony S Yu
6272c312c4
Remove `integral_images` and `integral_image_sqr` from _template.pyx in favor of calls to `skimage.transform.integral_image`.
This change required `match_template` arguments ("image" and "template") to be changed from float to double.
After this change, the template test runs about 25% slower.
69 lines
1.8 KiB
Python
69 lines
1.8 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. Nevertheless, this example concludes with a simple peak extraction
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algorithm to quantify the locations of matches.
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"""
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import numpy as np
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from skimage.detection 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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plt.gray()
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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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# 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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plt.figure()
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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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# Match the template.
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result = match_template(image, target, method='norm-corr')
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plt.figure()
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plt.imshow(result)
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plt.title("Result from ``match_template``")
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plt.axis('off')
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plt.show()
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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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assert np.all(found_positions == target_positions)
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