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Johannes Schönberger
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
"""
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=======================
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BRIEF binary descriptor
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=======================
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This example demonstrates the BRIEF binary description algorithm.
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The descriptor consists of relatively few bits and can be computed using
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a set of intensity difference tests. The short binary descriptor results
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in low memory footprint and very efficient matching based on the Hamming
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distance metric.
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However, BRIEF does not provide rotation-invariance and scale scale-invariance
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can be achieved by detecting and extracting features at different scales.
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The ORB feature detection and binary description algorithm is an extension to
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the BRIEF method and provides rotation and scale-invariance, see
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`skimage.feature.ORB`.
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"""
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from skimage import data
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from skimage import transform as tf
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from skimage.feature import (match_descriptors, corner_peaks, corner_harris,
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plot_matches, BRIEF)
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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.lena())
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tform = tf.AffineTransform(scale=(1.2, 1.2), translation=(0, -100))
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img2 = tf.warp(img1, tform)
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img3 = tf.rotate(img1, 25)
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keypoints1 = corner_peaks(corner_harris(img1), min_distance=5)
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keypoints2 = corner_peaks(corner_harris(img2), min_distance=5)
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keypoints3 = corner_peaks(corner_harris(img3), min_distance=5)
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extractor = BRIEF()
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descriptors1, mask1 = extractor.extract(img1, keypoints1)
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descriptors2, mask2 = extractor.extract(img2, keypoints2)
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descriptors3, mask3 = extractor.extract(img3, keypoints3)
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keypoints1 = keypoints1[mask1]
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keypoints2 = keypoints2[mask2]
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keypoints3 = keypoints3[mask3]
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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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plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13)
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ax[1].axis('off')
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plt.show()
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