60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
"""
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==========================================
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ORB feature detector and binary descriptor
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==========================================
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This example demonstrates the ORB feature detection and binary description
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algorithm. It uses an oriented FAST detection method and the rotated BRIEF
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descriptors.
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Unlike BRIEF, ORB is comparatively scale and rotation invariant while still
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employing the very efficient Hamming distance metric for matching. As such, it
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is preferred for real-time applications.
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"""
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from skimage import data
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from skimage import transform
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from skimage.feature import (match_descriptors, corner_harris,
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corner_peaks, ORB, plot_matches)
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from skimage.color import rgb2gray
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import matplotlib.pyplot as plt
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img1 = rgb2gray(data.astronaut())
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img2 = transform.rotate(img1, 180)
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tform = transform.AffineTransform(scale=(1.3, 1.1), rotation=0.5,
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translation=(0, -200))
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img3 = transform.warp(img1, tform)
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descriptor_extractor = ORB(n_keypoints=200)
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descriptor_extractor.detect_and_extract(img1)
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keypoints1 = descriptor_extractor.keypoints
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descriptors1 = descriptor_extractor.descriptors
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descriptor_extractor.detect_and_extract(img2)
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keypoints2 = descriptor_extractor.keypoints
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descriptors2 = descriptor_extractor.descriptors
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descriptor_extractor.detect_and_extract(img3)
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keypoints3 = descriptor_extractor.keypoints
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descriptors3 = descriptor_extractor.descriptors
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matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
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matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)
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fig, ax = plt.subplots(nrows=2, ncols=1)
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plt.gray()
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plot_matches(ax[0], img1, img2, keypoints1, keypoints2, matches12)
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ax[0].axis('off')
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ax[0].set_title("Original Image vs. Transformed Image")
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plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13)
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ax[1].axis('off')
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ax[1].set_title("Original Image vs. Transformed Image")
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plt.show()
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